Find Your Dream Machine Learning Job in India

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This is a remote, part-time position for a Senior Machine Learning Engineer specializing in MLOps and Data Engineering. The role involves designing, building, and maintaining scalable machine learning workflows, developing and optimizing models, and implementing state-of-the-art algorithms. The candidate will collaborate with cross-functional teams and contribute to the architecture of data pipelines and model deployment strategies. Additional responsibilities include analyzing data patterns, ensuring the performance and reliability of ML systems, and participating in the continuous improvement of deployed models.

• Strong foundation in Computer Science with expertise in Statistics and Algorithms.
• Advanced understanding of Machine Learning, including Pattern Recognition and Neural Networks.
• Proficiency in developing and optimizing machine learning models and deploying them at scale.
• Experience with distributed systems, data pipelines, and cloud platforms.
• Team collaboration and communication skills, with the ability to work independently in a remote environment.
• A Master's or Ph.D. in Computer Science, Data Science, or a related field is a strong advantage.

Company Description

Dream Opportunity is a global organization focused on expanding access to high-income career pathways for under-resourced students.

We partner with multi-billion-dollar companies, including JPMorgan Chase, AT&T, EY, Blackstone, Paramount, Informa, Kellogg's, Kellanova, and others to provide students with real exposure to careers and professional networks.

Building on this foundation, we are launching Dreamari, a global AI-powered, gamified ed-tech platform designed to help students discover careers, build skills, and unlock opportunity at scale.

Dreamari will launch with immediate distribution across eight countries: the United States, the United Kingdom, Canada, India, the Philippines, Mexico, Singapore, and the UAE.

We are forming the first selected core technical team to build this platform from the ground up. These roles will influence product architecture, user experience, global scalability, and long-term technical direction.

Role Overview

As a Machine Learning Engineer, you will build and scale the intelligence layer of the platform. You will design and deploy the recommendation, personalization, and inference systems that power tailored guidance for students globally.

This is a founding engineering role with direct influence on model architecture, production deployment, and long-term system scalability.

What You'll Do

• Design, train, and deploy production-grade machine learning models
• Build and iterate on recommendation and personalization systems
• Implement real-time inference pipelines across global user bases
• Optimize models for latency, reliability, and cloud efficiency
• Integrate embeddings, LLM APIs, and vector search systems where appropriate
• Monitor, evaluate, and continuously improve deployed models in production
• Ensure responsible, ethical, and transparent AI system design

Required Skills & Experience

• 5+ years of professional experience in machine learning or ML engineering roles
• Strong proficiency in Python
• Experience with PyTorch, TensorFlow, or comparable ML frameworks
• Demonstrated experience deploying production ML systems
• Experience with real-time inference systems
• Familiarity with embeddings, LLM APIs, and vector databases
• Experience deploying ML systems on cloud platforms (GCP, Vertex AI, or equivalent)
• Strong communication skills and ability to collaborate in a fast-moving startup environment

Nice to Have

• Experience building large-scale recommendation systems
• Experience optimizing ML systems for high concurrency and low latency
• Background in applied NLP or conversational AI

Compensation & Benefits

Compensation: 45-48 LPA (approximately $51,000 USD annually)

Equity: Meaningful equity participation as an early founding engineer

Role Type: Full-time, founding technical role

Ownership: High autonomy and real influence over technical decisions

Growth: Opportunity to build a global AI system used across multiple countries from day one

About the Company

Ema is building the next generation AI technology to empower every employee in the enterprise to be their most creative and productive. Our proprietary tech allows enterprises to delegate most repetitive tasks to Ema, the AI employee. We are founded by ex-Google, Coinbase, Okta executives and serial entrepreneurs. We've raised capital from notable investors such as Accel Partners, Naspers, Section32 and a host of prominent Silicon Valley Angels including Sheryl Sandberg (Facebook/Google), Divesh Makan (Iconiq Capital), Jerry Yang (Yahoo), Dustin Moskovitz (Facebook/Asana), David Baszucki (Roblox CEO) and Gokul Rajaram (Doordash, Square, Google). Our team is a powerhouse of talent, comprising engineers from leading tech companies like Google, Microsoft Research, Facebook, Square/Block, and Coinbase. All our team members hail from top-tier educational institutions such as Stanford, MIT, UC Berkeley, CMU and Indian Institute of Technology. We're well funded by the top investors and angels in the world. Ema is based in Silicon Valley and Bangalore, India. This will be a hybrid role where we expect employees to work from office three days a week.

About the Role

We're looking for an innovative and passionate Machine Learning Engineers to join our team. You are someone who loves solving complex problems, enjoys the challenges of working with huge data sets, and has a knack for turning theoretical concepts into practical, scalable solutions. You are a strong team player but also thrive in autonomous environments where your ideas can make a significant impact. You love utilizing machine learning techniques to push the boundaries of what is possible within the realm of Natural Language Processing, Information Retrieval and related Machine Learning technologies. Most importantly, you are excited to be part of a mission-oriented high-growth startup that can create a lasting impact.

Responsibilities

• Conceptualize, develop, and deploy machine learning models that underpin our NLP, retrieval, ranking, reasoning, dialog and code-generation systems.
• Implement advanced machine learning algorithms, such as Transformer-based models, reinforcement learning, ensemble learning, and agent-based systems to continually improve the performance of our AI systems.
• Lead the processing and analysis of large, complex datasets (structured, semi-structured, and unstructured), and use your findings to inform the development of our models.
• Work across the complete lifecycle of ML model development, including problem definition, data exploration, feature engineering, model training, validation, and deployment.
• Implement A/B testing and other statistical methods to validate the effectiveness of models.
• Ensure the integrity and robustness of ML solutions by developing automated testing and validation processes.
• Clearly communicate the technical workings and benefits of ML models to both technical and non-technical stakeholders, facilitating understanding and adoption.

Qualifications

• A Master's degree or Ph.D. in Computer Science, Machine Learning, or a related quantitative field.

Required Skills

• Proven industry experience in building and deploying production-level machine learning models.
• Deep understanding and practical experience with NLP techniques and frameworks, including training and inference of large language models.
• Deep understanding of any of retrieval, ranking, reinforcement learning, and agent-based systems and experience in how to build them for large systems.
• Proficiency in Python and experience with ML libraries such as TensorFlow or PyTorch.
• Excellent skills in data processing (SQL, ETL, data warehousing) and experience working with large-scale data systems.
• Experience with machine learning model lifecycle management tools, and an understanding of MLOps principles and best practices.
• Familiarity with cloud platforms like GCP or Azure.
• Familiarity with the latest industry and academic trends in machine learning and AI, and the ability to apply this knowledge to practical projects.
• Good understanding of software development principles, data structures, and algorithms.
• Excellent problem-solving skills, attention to detail, and a strong capacity for logical thinking.
• The ability to work collaboratively in an extremely fast-paced, startup environment.

Equal Opportunity Statement

Ema Unlimited is an equal opportunity employer and is committed to providing equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, gender identity, or genetics.

Company Description

Pandita AI Inc is a leader in machine learning innovation, creating products that revolutionize how industries and individuals leverage data and AI technologies. Our solutions are recognized for their excellence and forward-thinking approach, establishing us as a trusted partner across multiple sectors. We are dedicated to advancing the field of AI and fostering impactful transformations in the technological landscape. At Pandita AI Inc, we value collaboration, innovation, and delivering meaningful results.

Role Description

This is a full-time on-site role located in Mysore. The Sr. Machine Learning Scientist will engage in designing and implementing cutting-edge machine learning algorithms, conducting advanced research in artificial intelligence, and developing novel solutions for complex problems. Responsibilities include working with large-scale datasets, experimenting with neural networks, and contributing to the creation of impactful AI-driven products. Collaboration with cross-functional teams to ensure successful project execution is also a key aspect of this role.

Qualifications

• Strong expertise in Machine Learning, including Pattern Recognition and Neural Networks
• Solid foundation in Computer Science and Statistics
• Proficiency in Programming with Python. Knowledge of using claude, streamlit.
• Outstanding analytical and problem-solving skills, with an ability to independently drive research initiatives
• PhD or Research Masters in Computer Science, Artificial Intelligence, Data Science, or a related field
• Experience in working with large-scale datasets and developing scalable ML solutions
• Strong communication and collaboration skills with a research-focused mindset
• Publications are a must for this position.

Position: Machine Learning Engineer - Graph AI/Neo4j

Location: Hyderabad, India - Onsite - 5 days a week

Experience: 2+ years

Employment Type: Full-time

Position Overview:

We are seeking a talented and experienced Machine Learning Engineer with a strong background in graph databases, particularly Neo4j, to join our dynamic team. The ideal candidate will be instrumental in developing and enhancing our knowledge bases and Retrieval-Augmented Generation (RAG) models, driving the accuracy and efficiency of our AI-powered solutions. You will play a key role in deploying cutting-edge models that enhance the AI features of our end-user applications, ensuring they meet the evolving needs of our customers.

Key Responsibilities

• Develop and support machine learning models with a focus on graph-based data and Neo4j.
• Build and maintain Python scripts and data pipelines for processing and analyzing graph data.
• Work with Large Language Models (LLMs) and retrieval-augmented generation (RAG) techniques as part of the ML workflow.
• Collaborate with backend and data teams to integrate graph AI solutions into applications.
• Write clean, reusable code and participate in code reviews.
• Support deployment and basic monitoring of ML models in production.
• Document workflows and solutions for team knowledge sharing.

Must-Have Qualifications

• 2+ years of experience in Machine Learning or Data Science using Python.
• Experience working with at least one graph database (preferably Neo4j) for data modeling and basic queries.
• Good understanding of machine learning fundamentals (regression, classification, basic model evaluation).
• Exposure to using or integrating LLMs (OpenAI, HuggingFace, or similar) with data workflows.
• Basic knowledge of retrieval-augmented generation (RAG) concepts.
• Familiarity with Python data libraries (pandas, scikit-learn, etc.).
• Ability to work with RESTful APIs.
• Familiarity with version control (Git) and writing simple unit tests.

Nice to Have

• Hands-on experience building or optimizing graph ML models (e.g., node classification, link prediction).
• Exposure to vector search or hybrid search techniques.
• Experience deploying Python code or ML models using Docker or basic cloud services (AWS, GCP, Azure).
• Experience working in a SaaS or multi-tenant application environment.

Key Skills

Python, Machine Learning, Graph Databases (Neo4j), LLM, RAG, Data Pipelines, Git, REST API

Looking for . ML Testing & Validation

• Develop test strategies for ML models

• Validate ML outputs using metrics such as Accuracy,

Python Automation & Framework Development

• Design and implement a scalable Python-based ML QA framework using:

• Excellent Python programming (3+ years)

• Strong in Pandas, NumPy, PyTest/unittest

Work Location: Bangalore

Work Mode: Hybrid

Experience: 5+ Years

Job Description:

• Build, deploy, and scale machine learning platforms and distributed ML workloads.
• Ray.io is a key platform, but candidates do not need prior Ray experience if fundamentals are strong.

Core Requirements (Must-Have):

• Strong experience as an ML Platform Engineer
• Hands-on experience with:
• TensorFlow
• PyTorch
• LLMs / generative AI models
• Proven ability to:
• Build ML pipelines
• Deploy models into production
• Run ML workloads at scale
• Strong Kubernetes knowledge (mandatory)
• Solid understanding of distributed systems and cloud-native architecture

Ray.io Expectations:

• Prior Ray experience is a plus, not a blocker
• Candidate should be capable of learning Ray quickly ( 1 month ramp-up)
• Focus is on using Ray for:
• Distributed training
• Parallel processing
• Scalable ML workloads

Who we are

Wayfair is moving the world so that anyone can live in a home they love - a journey enabled by more than 3,000 Wayfair engineers and a data-centric culture. The Customer Technology(CT) team builds and owns all the Machine learning (ML) products that power all search, marketing, and recommendations technology across Wayfair. Our algorithms tackle a varied & broad spectrum of challenges in the Wayfair marketplace; from empowering suppliers to easily add products to our catalog, to enabling our customers to discover and purchase a vast & diverse assortment of home goods, to driving marketing experiences customers love all at web scale.

We are looking for machine-learning scientists for research and development of the ML systems that power our real-time Search Typeahead, Search Guidance, Query Understanding & Interpretation, Query Refinement and Recommendations, and emerging GenAI-powered search capabilities. In this role, you'll partner with fellow scientists, engineers, analysts, and product managers to apply science and machine learning skills to directly impact Wayfair's revenue.

What you'll do

• Design, build, deploy and refine large-scale machine learning models and algorithmic decision-making systems that solve real-world problems for customers
• Work cross-functionally with commercial stakeholders to understand business problems or opportunities and develop appropriately scoped analytical solutions
• Collaborate closely with various engineering, infrastructure, and ML platform teams to ensure adoption of best-practices in how we build and deploy scalable ML services
• Identify new opportunities and insights from the data (where can the models be improved? what is the projected ROI of a proposed modification?)
• Be obsessed with the customer and maintain a customer-centric lens in how we frame, approach, and ultimately solve every problem we work on.

What you'll need

• 3+ years of industry experience with a Bachelor/ Master's degree or minimum of 1-2 years of industry experience with PhD in Computer Science, Mathematics, Statistics, or related field.
• Strong theoretical understanding of statistical models such as regression, clustering and ML algorithms such as decision trees, neural networks, etc.
• Solid understanding of algorithms for search retrieval, ranking, relevance, query understanding, NLP, or recommender systems.
• Good proficiency in the Python ML ecosystem (pandas, NumPy, scikit-learn, XGBoost, etc.).
• Solid hands-on expertise deploying machine learning solutions into production
• Strong written and verbal communication skills.
• Intellectual curiosity and enthusiasm about continuous learning.

Nice to have

• Experience in e-commerce, or online search systems.
• Knowledge of modern NLP techniques (transformers, embeddings, semantic search).
• Experience applying Generative AI / LLMs (e.g., GPT, LLaMA) to real-world problems such as search, personalization, or content generation.
• Experience with deep learning frameworks like PyTorch.
• Familiarity with vector databases, information retrieval frameworks (e.g., Elasticsearch, Vespa)
• Familiarity with GCP (or AWS, Azure), ML model development frameworks, ML orchestration tools (Airflow, Kubeflow or MLFlow)
• Experience with Apache Spark Ecosystem (Spark SQL, MLlib/Spark ML).

Role: Machine Learning Engineer II

Location: Bengaluru

We're MiQ, a global programmatic media partner for marketers and agencies.

Our people are at the heart of everything we do, so you will be too. No matter

the role or the location, we're all united in the vision to lead the programmatic

industry and make it better.

What you'll bring

-2 - 4 years of hands-on experience deploying and monitoring Machine Learning Models in production.

-Strong proficiency in Python and familiarity with distributed data processing frameworks like PySpark.

-Excellent grasp of the basics of Machine Learning, Statistics and Probability.

-Experience with building scalable ETL pipelines for efficient storage, processing and retrieval of features to support ML model development.

-Experience building and deploying in production end to end model training, inferencing and retraining pipelines .

-Experience with data and model monitoring to ensure consistency and reliability of solutions in production.

-Familiarity with standard best practices like version control and CI / CD for code management, unit testing and quality code standards.

-Experience working in cross functional teams, consisting of Data Scientists, Data Engineers and Product Managers to deliver quality solutions that meet business goals.

-Passion for self driven learning and an eagerness to keep up with continuously evolving MLOps / LLMOps technologies and best practices.

-Hands on experience with distributed data processing frameworks like Ray.

-Hands on experience with designing and maintaining Feature Stores.

Values:

Our values are so much more than statements. They unite MiQers in every corner of the world. They shape the way we work and the decisions we make. And they inspire us to stay true to ourselves and to aim for better. Our values are there to be embraced by everyone, so that we naturally live and breathe them. Just like inclusivity, our values flow through everything we do - no matter how big or small.

• We do what we love - Passion

• We figure it out - Determination

• We anticipate the unexpected - Agility

• We always unite - Unite

• We dare to be unconventional - Courage

Benefits

Every region and office have specific perks and benefits, but every person joining MiQ can expect:

• A hybrid work environment

• New hire orientation with job specific onboarding and training

• Internal and global mobility opportunities

• Competitive healthcare benefits

• Bonus and performance incentives

• Generous annual PTO paid parental leave, with two additional paid days to acknowledge holidays, cultural events, or inclusion initiatives.

• Employee resource groups designed to connect people across all MiQ regions, drive action, and support our communities.

Apply today!

Equal Opportunity Employer

Job Overview:

We are seeking an experienced Senior Machine Learning Engineer (Computer Vision) with 8-10 years of experience in building, deploying, and optimizing ML and computer vision solutions at scale. The role focuses on developing end-to-end ML systems grounded in strong data science fundamentals, while increasingly integrating Gen AI capabilities such as RAG, multimodal models, and agent-based workflows.

The candidate will work closely with product, platform, and MLOps teams to design reliable, scalable ML components that move from experimentation to production, ensuring performance, accuracy, and long-term maintainability.

Key Responsibilities:

• Design, develop, and deploy computer vision and machine learning models for real-world business use cases.
• Apply strong data analysis, statistical reasoning, and feature engineering to improve model performance and robustness.
• Build and optimize deep learning models using CNNs and Transformer-based architectures for vision and multimodal tasks.
• Own the end-to-end ML lifecycle, including data preparation, training, evaluation, tuning, and production deployment.
• Collaborate with MLOps teams to support model versioning, monitoring, retraining, and performance tracking in production.
• Design ML components that integrate seamlessly into larger application and platform architectures.
• Contribute to GenAI initiatives, including building RAG pipelines that combine vision and text data, and integrating CV outputs into LLM workflows.
• Implement and evaluate single-agent and limited multi-agent workflows where applicable.
• Select appropriate evaluation metrics and clearly explain model trade-offs, limitations, and outcomes to stakeholders.
• Mentor junior engineers and contribute to knowledge sharing and best practices within the team.
• Collaborate closely with cross-functional teams including product, data, platform, and engineering to deliver end-to-end solutions.

Key Skills and Qualifications:

• Strong foundation in machine learning, deep learning, and data science principles, including supervised and unsupervised learning.
• Hands-on experience with computer vision models, including CNNs and Transformer-based architectures.
• Proficiency in Python and common ML/data libraries, with solid understanding of data structures and algorithms.
• Experience with SQL, data pre-processing, and feature engineering for structured and unstructured datasets.
• Practical experience deploying ML models into production environments and handling challenges such as data imbalance, drift, and noisy labels.
• Familiarity with cloud-based ML workflows on platforms such as AWS, Azure.
• Exposure to distributed training or large-scale data processing environments.
• Working knowledge of MLOps practices, including monitoring, retraining, and experiment tracking.
• Experience with GenAI concepts, including RAG systems, multimodal pipelines, lightweight fine-tuning, and evaluation of LLM outputs.
• Strong communication skills with the ability to explain complex technical concepts to both technical and non-technical stakeholders.
• Experience in Edge based deployment on AI models ex CoreML for iOS, and TensorFlow Lite (TFLite) for mobile deployment.
• Knowledge about Hardware Acceleration Experience with NVIDIA Jetson, Coral Edge TPU, or mobile NPUs (Neural Processing Units).
• Video Orchestration: GStreamer or FFmpeg for low-latency camera stream ingestion.
• Frameworks: PyTorch or TensorFlow; TorchVision for vision-specific primitives.
• Inference Optimization using NVIDIA TensorRT, OpenVINO, or TFLite for low-latency performance.
• Experience in sports domain will be a plus.

Educational Background:

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related field.
• Advanced certifications such as AWS Certified Machine Learning - Specialty ,Microsoft Certified: Azure AI Engineer Associate are good to have.
• Specialized training in Machine Learning, AI, or Cloud ML platforms are a plus.

ML Engineer

Experience-5+ Years

Immediate Joiners Only

Location : Hyderabad

Job Description:

We are seeking an experienced Machine Learning Engineer to design, build, deploy, and maintain scalable ML systems in a production environment. This role involves close collaboration with data scientists, ownership of ML pipelines, and hands-on work with Google Cloud Platform. The ideal candidate will have strong experience in MLOps, production deployments, monitoring, and ML systems reliability.

Key Responsibilities:

Build and maintain reliable data pipelines

Prepare clean, structured datasets for machine learning

Work with data from client's data warehouse and other internal sources

Use transformation tools like dbt when needed

Automate ML training workflows on Google Cloud Platform

Build reproducible ML pipelines

Work with data scientists to turn experimental models into production-ready systems

Deployment & Serving

Deploy ML models using Cloud Run, Kubernetes, and Vertex AI

Build and maintain REST APIs in Python to serve predictions

Ensure models are fast, stable, and secure

Set up dashboards with DataDog, Grafana, or similar tools

Monitor model performance, accuracy, data drift, and system health

Troubleshoot issues and ensure smooth operation in production

Required Skills and Qualifications:

5+ years of experience building and deploying ML models in production

Strong Python skills (pipelines, training workflows, APIs)

Experience with ML frameworks (TensorFlow, PyTorch, scikit-learn, etc.)

Solid understanding of MLOps practices: CI/CD, model versioning, automated pipelines, monitoring

Hands-on experience with GCP services: Cloud Run, Kubernetes, BigQuery, Vertex AI

Experience building APIs (FastAPI, Flask, or similar)

Good understanding of automated testing Monitoring: Experience with DataDog or Grafana Knowledge of ethical AI, bias mitigation, or data privacy principles Knowledge of AI guardrailing Experience with recommender systems Vector databases Embeddings Feature engineering Matrix factorization techniques Ttwin tower models Basic experience or exposure to recommender systems Monitoring and observability of models in production User feedback handling Model training and evaluation (at least theoretical understanding) A/B testing Performance metrics

Designation - Lead ML Engineer:

We're looking to hire someone to help scale Machine Learning and Artifi cial Intelligence efforts at Paisabazaar. You'll work with the team that develops the models that shall power Paisabazaar's products focused on Speech, NLP, Computer Vision and generative AI. Some of the problems include Automated Speech Recognition(ASR) of Hinglish customer-agent audio conversations and generating critical insights for business, building gen AI chatbots on top of the rich knowledge bases, leverage document intelligence for intelligent spend analytics that further power the chat layer of Paisabazaar.

This is a role for highly technical machine learning who combine outstanding oral and written communication skills, and the ability to code up prototypes and productionalize using a large range of tools, algorithms, and languages. Most importantly they need to have the ability to autonomously plan and organize their work assignments based on high-level team goals.

You will be responsible for developing machine learning models that positively impact the business, working with partners across the company including operations, product, and engineering. You will use research results to shape strategy for the company and help build a foundation of tools and practices used by quantitative staff across the company.

Required Skills and Qualification:

Strong background in classical machine learning and machine learning deployments is a must and preferably with 8+ years of experience.

Knowledge of deep learning, specializing in any of the following: Speech, NLP, Computer and generative AI.

Hands-on experience in PyTorch, Python, cloud (AWS/Azure/GCP) & Big Data platforms to manipulate large-scale structured and unstructured datasets.

Experience with GPU computing is a plus.

Expert-level experience with a wide range of quantitative methods that can be applied to business problems.

Excellent written and verbal communication skills on quantitative topics for a variety of audiences: product managers, designers, engineers, and business leaders.

Fluent in data fundamentals: SQL, data manipulation using a procedural language, statistics, experimentation and modeling.

Skills:- Data Science, Python, Azure/GCP/AWS, Apache Spark, Machine Learning (ML), NLP, Speech Recognition and Generative AI.

Company Description

Ai Health Highway is a global team of medical professionals, signal processing engineers, data scientists, and clinicians. The company is AI-first and focused on making screening of chronic diseases cost-effective at the primary care clinic. This approach aims to improve patient outcomes and accessibility to healthcare services.

We are backed by Turbostart, Rainmatter by Zerodha, The Chennai Angels, Social Alpha, Foundation for Science, Innovation & Development (FSID) IISC and many other prominent angel investors. AiSteth is one of the 27 most promising ML startups selected for AWS ML Elevate 2022 program by Amazon, Intel, Accel & YourStory. We are also the winners of the PHC Tech Challenge 2021 award organized by PATH.

Goal - Our Goal is to reduce 30% premature deaths due to by 2030.

Role Description - ML Engineer (Bangalore / On-Site)

• Work closely with the Product leader to define product/platform vision
• Interface with Client ML and Business teams to demonstrate thought leadership, and drive delivery of products and solutions
• Conduct original research on large proprietary and open source data sets
• Identify, research, prototype and build predictive models
• Create framework for AI/ML code deployment to ensure robustness and reliability of production ready models
• Responsible for understanding industry processes and incorporating them in the solution
• Be responsible for measuring and optimizing the quality of your algorithms
• Collaborate with engineering teams, platform teams to drive vision for AI/ML platform

Qualifications

• Minimum 2-3 years of experience working in similar field/ domain.
• Advanced degree in Computer Science, Signal Processing, Data science, Biomedical Engineering, AI/ML
• At least one core programming expertise, such as python (Tensorflow, Keras, Pytorch, Signal Processing packages etc.), R, Scala
• Strong statistical knowledge, analytical and problem-solving skills, intuition and experience applying machine learning models to real world data
• Strong project management skills to execute multiple projects in parallel
• Experience in deploying production ready ML code.
• Experience with knowledge graphs, optimization, decision theory or signal processing
• Interpersonal skills: good verbal and written communication skills, cross-group and cross-culture collaboration.
• Understanding of Algorithms and Machine Learning principles
• Experience with healthcare data analysis is a plus

Job Title: ML Engineer

Location: Noida

Experience Required: 3-6 years

About the Job

We are looking for a Machine Learning Engineer to design, build, and deploy ML models that power intelligent agent systems and enterprise AI solutions.

The Machine Learning Engineer will be responsible for developing scalable ML pipelines, optimizing models for performance, and ensuring robust deployment in production environments. This role requires strong technical expertise in ML algorithms and practical experience in MLOps.

Responsibilities

Hands-on experience in deploying ML models at scale with automated monitoring and retraining in production environments.

Design and implement machine learning models for prediction, classification, and optimization tasks.

Build and maintain scalable ML pipelines for data ingestion, training, and deployment.

Collaborate with data scientists and engineers to integrate models into agent workflows.

Monitor and improve model performance post-deployment using automated retraining strategies.

Ensure compliance with best practices for security, reliability, and ethical AI.

Mandatory Skills

Proficiency in Python, TensorFlow, and PyTorch.

Experience with ML pipelines and MLOps tools (MLflow, Kubeflow).

Strong understanding of data preprocessing, feature engineering, and model evaluation metrics.

Familiarity with cloud platforms (AWS, Azure, GCP) for ML deployment.

Additional Skills

Knowledge of LLM integration and hybrid AI systems.

Experience with containerization (Docker) and orchestration (Kubernetes).

Exposure to CI/CD pipelines for ML workflows.

Designation - Lead ML Engineer:

We're looking to hire someone to help scale Machine Learning and Artifi cial Intelligence efforts at Paisabazaar. You'll work with the team that develops the models that shall power Paisabazaar's products focused on Speech, NLP, Computer Vision and generative AI. Some of the problems include Automated Speech Recognition(ASR) of Hinglish customer-agent audio conversations and generating critical insights for business, building gen AI chatbots on top of the rich knowledge bases, leverage document intelligence for intelligent spend analytics that further power the chat layer of Paisabazaar.

This is a role for highly technical machine learning who combine outstanding oral and written communication skills, and the ability to code up prototypes and productionalize using a large range of tools, algorithms, and languages. Most importantly they need to have the ability to autonomously plan and organize their work assignments based on high-level team goals.

You will be responsible for developing machine learning models that positively impact the business, working with partners across the company including operations, product, and engineering. You will use research results to shape strategy for the company and help build a foundation of tools and practices used by quantitative staff across the company.

Required Skills and Qualification:

Strong background in classical machine learning and machine learning deployments is a must and preferably with 8+ years of experience.

Knowledge of deep learning, specializing in any of the following: Speech, NLP, Computer and generative AI.

Hands-on experience in PyTorch, Python, cloud (AWS/Azure/GCP) & Big Data platforms to manipulate large-scale structured and unstructured datasets.

Experience with GPU computing is a plus.

Expert-level experience with a wide range of quantitative methods that can be applied to business problems.

Excellent written and verbal communication skills on quantitative topics for a variety of audiences: product managers, designers, engineers, and business leaders.

Fluent in data fundamentals: SQL, data manipulation using a procedural language, statistics, experimentation and modeling.

Skills:- Data Science, Python, Azure/GCP/AWS, Apache Spark, Machine Learning (ML), NLP, Speech Recognition and Generative AI.

Position: AI / Machine Learning Engineer - Intelligent Automation

Experience: 5+ Years

Location: Ahmedabad, Gujarat

Company: MSBC Group

MSBC Group is seeking an experienced AI / Machine Learning Engineer to design and deliver enterprise-grade AI agents and intelligent automation solutions that enhance software engineering productivity. This role focuses on building scalable, production-ready AI systems used across development, QA, and DevOps functions.

The position offers an opportunity to work at the intersection of artificial intelligence, workflow automation, and software engineering, contributing to solutions with measurable business and operational impact.

Key Responsibilities

• Design and develop AI agents to automate software engineering and operational workflows
• Analyze existing engineering processes to identify and implement automation opportunities
• Build ML- and LLM-based solutions for task planning, reasoning, and decision support
• Integrate AI capabilities into developer tools and internal platforms
• Deploy, monitor, and continuously optimize AI systems in production environments
• Collaborate with engineering and product teams to ensure alignment with business objectives

Required Skills:

• Minimum 5 years of experience in AI/ML or applied machine learning
• Strong proficiency in Python with solid software engineering fundamentals
• Hands-on experience with LLMs, NLP, and AI agent architectures
• Proven experience delivering production-grade ML or automation systems
• Understanding of workflow automation and task-oriented system design

Preferred Skills:

• Experience automating software development or IT operations workflows
• Familiarity with AI agent frameworks, RAG architectures, and prompt engineering
• Background in DevOps, QA automation, or internal developer tooling

Why MSBC

• Opportunity to build practical AI solutions with real enterprise adoption
• Work on mission-critical automation initiatives, not experimental prototypes
• Collaborate with experienced engineering teams in a structured, professional environment
• Contribute to shaping how AI is integrated into modern software development practices

Apply via Email: or reach out via LinkedIn with your profile.

Who We Are

Wayfair is an online retail platform with the mission to enable everyone to live in a home they love. To do this, Wayfair builds and operates cutting-edge machine learning and AI systems at massive scale. Our Search and Recommendations ML teams develop algorithmic systems that drive business performance, enhance customer experience, and build long-term customer loyalty. These systems power what customers see both on and off the Wayfair platform and address a broad spectrum of challenges across the marketplace-from helping suppliers efficiently add products to our catalog, to enabling customers to discover and purchase from a vast and diverse assortment of home goods, all at web scale. You will be part of a highly collaborative, cross-functional environment building ML systems with real-world impact.

We are seeking an experienced machine learning manager to lead the research and development of the ML systems that power Wayfair's personalized customer experiences, including product recommendations and search. In this role, you will be accountable for defining and executing the technical roadmap, building and scaling high-performing ML teams, and pursuing innovative ideas that shape the next generation of Wayfair's ML stack. You will work closely with engineering, analytics, and product leadership within the Customer Technology organization to define technical vision and translate it into execution. This role is a cornerstone of the Search and Recommendations organization, with broad visibility and close collaboration with business and technology leaders up to the executive level.

What You'll Do

• Lead the development of machine learning algorithms in Wayfair's algorithmic customer understanding, content and product recommendations for powering personalization.
• Grow and manage a team of ML scientists and machine-learning engineers.
• Own the forward looking roadmap and executional plan for a team and be accountable for the team plan to achieve this ambitious roadmap.
• Develop technical leadership within the team and continuously up-skill the team while modelling a culture of continuous research and innovation.
• Collaborate closely with the product and engineering leaders in our Customer Technology group to build a next-generation personalisation stack for search and recommendations and the organization required to maintain and innovate on it
• Contribute as a senior leader beyond the Customer Technology group, in particular within Wayfair's broader ML science community

What You'll Need

• Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, or related field.
• 10+ years of industry experience, with at least 1-2 years experience as a manager of teams and 7+ serving as an IC on production ML systems.
• Track record of leading teams and building scalable customer-facing machine learning products that work in real-time with low latency and drive tangible and material business results.
• Deep familiarity with search and ranking, or content recommendations as well as experience building scalable applications around technology such as Spark/Python/Airflow/GCP or AWS and Java.
• Ability to partner cross-functionally to own and shape technical roadmaps and the organizations required to drive them.
• Intellectual curiosity and a desire to always be learning!

Nice to have

• PhD in Computer Science, Data Science or related field
• Familiarity with Generative AI and their usage in improving search and recommender systems.
• Familiarity with GCP (or AWS, Azure), ML model development frameworks, ML orchestration tools (Airflow, Kubeflow or MLFlow)

What you will be doing

We are looking for a Data Scientist who can operate at the intersection of classical machine learning, large-scale recommendation systems, and modern agentic AI systems.

You will design, build, and deploy intelligent systems that power Glance's personalized lock screen and live entertainment experiences. This role blends deep ML craftsmanship with forward-looking innovation in autonomous/agentic systems.

Your responsibilities will include:

Classical ML & Recommendation Systems

• Design and develop large-scale recommendation systems using advanced ML, statistical modeling, ranking algorithms, and deep learning.
• Build and operate machine learning models on diverse, high-volume data sources for personalization, prediction, and content understanding.
• Develop rapid experimentation workflows to validate hypotheses and measure real-world business impact.
• Own data preparation, model training, evaluation, and deployment pipelines in collaboration with engineering counterparts.
• Monitor ML model performance using statistical techniques; identify drifts, failure modes, and improvement opportunities.

Agentic Systems & Next-Gen AI

• Build and experiment with agentic AI systems that autonomously observe model performance, trigger experiments, tune hyperparameters, improve ranking policies, or orchestrate ML workflows with minimal human intervention.
• Apply LLMs, embeddings, retrieval-augmented architectures, and multimodal generative models for semantic understanding, content classification, and user preference modeling.
• Design intelligent agents that can automate repetitive decision-making tasks-e.g., candidate generation tuning, feature selection, or context-aware content curation.
• Explore reinforcement learning, contextual bandits, and self-improving systems to power next-generation personalization.

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Project Description:

• We are hiring an ML Ops Engineer to operationalize machine learning solutions across Digital Oilfield (DOF) platforms. This is a critical role ensuring that models move from development to robust, production-grade deployment with high availability, traceability, and security.
• The ideal candidate will be responsible for automating pipelines, managing model versions, integrating APIs, and monitoring ML system health across diverse operational environments. Oil & gas exposure is beneficial but not required; this is a technical engineering role with high ownership and cross-team collaboration.

Responsibilities:

• • Develop and maintain ML pipelines for data preprocessing, training, validation, deployment, and monitoring.
• • Implement model governance strategies-versioning, rollback, audit trails, and explainability.
• • Set up containerized environments and scalable inference systems (e.g., using Docker, Kubernetes).
• • Collaborate with software, IT, and data teams to ensure model integration and compliance.
• • Support continuous improvement of ML lifecycle management practices and tooling.
• • Business trip to Kuwait for first 6-12 months. On-site

Mandatory Skills Description:

• • Strong software engineering background with ML Ops or DevOps exposure
• • Hands-on experience with MLflow, Azure ML, Kubeflow, or equivalent.
• • Proficiency in CI/CD workflows and scripting for automation.
• • Familiarity with model monitoring, drift detection, and alerting frameworks.
• • Experience deploying Python-based ML models in production.
• • Ready for a long term business trip to Kuwait for first 6-12 months

Nice-to-Have Skills Description:

• • Understanding of Digital Oilfield environments or OT/IT integration.
• • Familiarity with cloud-native services-Azure is preferred.
• • Exposure to real-time data streaming (e.g., Kafka, IoT platforms).
• • Understanding of security, compliance, and data access protocols in enterprise environments.
• Certifications:
• • Azure DevOps or Azure AI Engineer certification is a plus.
• • AWS DevOps Engineer certification is nice to have but not required

Languages:

• English: C1 Advanced

Who We Are

Wayfair is an online retail platform with the mission to enable everyone to live in a home they love. To do this, Wayfair builds and operates cutting-edge machine learning and AI systems at massive scale. Our Search and Recommendations ML teams develop algorithmic systems that drive business performance, enhance customer experience, and build long-term customer loyalty. These systems power what customers see both on and off the Wayfair platform and address a broad spectrum of challenges across the marketplace—from helping suppliers efficiently add products to our catalog, to enabling customers to discover and purchase from a vast and diverse assortment of home goods, all at web scale. You will be part of a highly collaborative, cross-functional environment building ML systems with real-world impact.

We are seeking an experienced machine learning manager to lead the research and development of the ML systems that power Wayfair’s personalized customer experiences, including product recommendations and search. In this role, you will be accountable for defining and executing the technical roadmap, building and scaling high-performing ML teams, and pursuing innovative ideas that shape the next generation of Wayfair’s ML stack. You will work closely with engineering, analytics, and product leadership within the Customer Technology organization to define technical vision and translate it into execution. This role is a cornerstone of the Searc

Company: LabelBlind®

Role: AI Engineer

Full-Time Role. Mumbai, India.

IMMEDIATE joining

LabelBlind is India’s 1st digital food labelling company. As a B2B SaaS startup focused on revolutionizing food labeling through innovative software solutions, our mission is to empower food businesses with tools that ensure compliance, accuracy, and efficiency in food labeling.

The company is based out of Mumbai, seeking Mumbai based candidates only.

Job Description:

We are seeking an experienced AI Engineer to join our dynamic team based in Mumbai. As an AI Engineer, you will play a crucial role in developing and deploying innovative AI solutions that enhance our food-related services. Your expertise in LLMs, RAG, NLP, and Generative AI will be instrumental in driving our company's success.

Role reporting to the VP - Engineering.

Responsibilities:

1. Develop and implement LLM systems to extract relevant information from food-related documents.
2. Design and train object detection models using state-of-the-art techniques to recognize and categorize food items under relevant data sets.
3. Utilize Natural Language Processing (NLP) techniques to extract insights from text data to improve our understanding of food products and enhance our services.
4. Collaborate with cross-functional teams, including softwa

Position: Machine Learning Engineer – Graph AI/Neo4j

Location: Hyderabad, India - Onsite - 5 days a week

Experience: 2+ years

Employment Type: Full-time

Position Overview:

We are seeking a talented and experienced Machine Learning Engineer with a strong background in graph databases, particularly Neo4j, to join our dynamic team. The ideal candidate will be instrumental in developing and enhancing our knowledge bases and Retrieval-Augmented Generation (RAG) models, driving the accuracy and efficiency of our AI-powered solutions. You will play a key role in deploying cutting-edge models that enhance the AI features of our end-user applications, ensuring they meet the evolving needs of our customers.

Key Responsibilities

• Develop and support machine learning models with a focus on graph-based data and Neo4j.
• Build and maintain Python scripts and data pipelines for processing and analyzing graph data.
• Work with Large Language Models (LLMs) and retrieval-augmented generation (RAG) techniques as part of the ML workflow.
• Collaborate with backend and data teams to integrate graph AI solutions into applications.
• Write clean, reusable code and participate in code reviews.
• Support deployment and basic monitoring of ML models in production.
• Document workflows and solutions for team

Mente Consultancies Pvt. Ltd. is looking for experienced Machine Learning and Programming Analysts at their office located in New Delhi. This is an on-site role.

The incumbent is expected to do the following: -

• Understanding the business problem and effectively devise data-driven solutions to solve the problems identified
• Work on programming assignments leveraging Python, R or SAS
• Work on design, development and testing of software programs or ML algorithms
• Work in a collaborative team environment under the guidance of a project leader and deliver the tasks assigned
• Be a self-starter and take the initiative for learning new technologies, algorithms through research etc.
• Be able to communicate effectively with the clients through oral and written mediums (power point, word) and interact with junior client stakeholders
• Use data and machine learning to deliver solutions for the clients

The incumbent should possess the below: -

• Graduation in any Engineering stream / MCA
• Worked on Programming languages. Knowledge of Python, R or SAS is a must
• Strong problem-solving skills
• Ability to work towards end product
• Should have cleared the UG/PG degree
• Relevant work experience of minimum 3 years

About Turing:

Based in San Francisco, California, Turing is the world’s leading research accelerator for frontier AI labs and a trusted partner for global enterprises deploying advanced AI systems. Turing supports customers in two ways: first, by accelerating frontier research with high-quality data, advanced training pipelines, plus top AI researchers who specialize in coding, reasoning, STEM, multilinguality, multimodality, and agents; and second, by applyingthat expertise to help enterprises transform AI from proof of concept into proprietary intelligence with systems that perform reliably, deliver measurable impact, and drive lasting results on the P&L

Role Overview:

We are looking for experienced Machine Learning Engineers (MLE Bench) to contribute to benchmark-driven evaluation projects focused on real-world machine learning systems. This role involves hands-on work with production-grade ML codebases, model training and evaluation pipelines, and deployment-oriented workflows to help assess and improve the capabilities of advanced AI systems.

The ideal candidate is comfortable bridging research and engineering, working deeply with models, data, and infrastructure in realistic ML environments.

What does day-to-day life look like?

• Work with real-world ML codebases to support MLE Bench–style evaluation tasks .
• Build, run, and modify model tra

Job Title: Executive – Machine Learning Ops. Engineer

Location: Andheri (Mumbai)

Department: IT

Employment Type: Full-Time

Industry - Pharmaceutical

Job Summary

We are seeking a skilled and detail-oriented MLOps Engineer with 3-4 years of experience to drive the deployment, monitoring, and operational excellence of AI agents and machine learning models within enterprise environments. The ideal candidate will be responsible for building robust CI/CD pipelines for AI systems, optimizing GPU workloads, managing hybrid infrastructure (on-premises and cloud), and ensuring performance, security, and compliance — particularly within a regulated pharma environment.

This role requires strong experience in productionizing ML/GenAI solutions and ensuring scalability, reliability, and compliance of AI systems handling sensitive data.

Key Responsibilities

1. Model & AI Agent Deployment

• Own end-to-end deployment of ML models and AI agents into production environments.
• Manage model versioning, rollback strategies, and lifecycle management.
• Ensure high availability, scalability, and reliability of deployed systems.

2. CI/CD for AI Systems

• Design and implement CI/CD pipelines tailored for ML and GenAI workflows.
• Automate model training, testing, validation, and deploy

Role: ML Engineer

Years of Experience: 12 to 18 Years

Location: Anywhere in India

Roles and Responsibilities:

Lead the design, development, and deployment of advanced AI and data science methodologies, including NLP, Machine Learning, and Deep Learning, to deliver scalable solutions for complex business challenges.

Oversee the end-to-end lifecycle of large-scale data science and AI projects, from data acquisition and feature engineering to model development, deployment, and post-production monitoring, ensuring measurable business value.

Provide strategic direction to the data science roadmap, identifying high-impact opportunities for innovation and guiding the team toward delivering actionable, production-ready insights.

Represent the data science team in executive forums, steering committees, and client engagements, advocating for data-driven decision-making and championing enterprise-wide adoption of applied AI solutions.

Collaborate with cross-functional teams—including product, engineering, and business stakeholders—to conceptualize, prototype, and implement scalable AI models across areas such as Natural Language Processing , Predictive Analytics, Recommender Systems, and Large Language Models ( LLMs ).

Design robust experimentation frameworks, validate algorithms, and ensure compliance with responsible AI practices around fairness

What you will be doing

We are looking for a Data Scientist who can operate at the intersection of classical machine learning , large-scale recommendation systems , and modern agentic AI systems .

You will design, build, and deploy intelligent systems that power Glance’s personalized lock screen and live entertainment experiences. This role blends deep ML craftsmanship with forward-looking innovation in autonomous/agentic systems.

Your responsibilities will include:

Classical ML & Recommendation Systems

• Design and develop large-scale recommendation systems using advanced ML, statistical modelling, ranking algorithms, and deep learning.
• Build and operate machine learning models on diverse, high-volume data sources for personalization, prediction, and content understanding.
• Develop rapid experimentation workflows to validate hypotheses and measure real-world business impact.
• Own data preparation, model training, evaluation, and deployment pipelines in collaboration with engineering counterparts.
• Monitor ML model performance using statistical techniques; identify drifts, failure modes, and improvement opportunities.

Agentic Systems & Next-Gen AI

• Build and experiment with agentic AI systems that autonomously observe model pe

Introduction DeCentralCode:

DeCentralCode is a technology company that applies modern solutions to address current challenges. With locations in Rotterdam, Netherlands and Salem, India, we operate on a global scale.

Our expertise lies in cutting-edge technologies such as machine learning/artificial intelligence, distributed ledger technology, blockchain, Internet of Things, cloud-native, serverless architecture, and cybersecurity.

We collaborate with knowledge institutions, governments, and businesses to co-create and validate solutions for various domains including supply chain and energy transition, among others.

As an early-stage partner, we offer the opportunity to work with the cutting-edge technologies from scratch.

Location:

Salem, Tamil Nadu, India.

We are looking for:

We’re looking for an ML/AI Engineer to own the complete machine learning pipeline for a medical imaging application — from dataset curation through model deployment. You’ll build classification systems, implement domain adaptation for real-world robustness, and create statistical validation infrastructure.

Ability to identify and solve complex problems using machine learning techniques, with a focus on delivering actionable insights and tangible business value.

Implement best practices for testing and validation of machine learning models, ensuring robustness

Mente Consultancies Pvt. Ltd. is looking for Machine Learning and Programming interns at their office located in New Delhi. The internship will vary with an initial period of 3 months to 6 months, including 2 weeks of induction training.

The incumbent will be paid during the internship period. This is an on-site role.

The incumbent is expected to do the following: -

· Work on programming assignments leveraging Java, Python, R and SAS

· Support project teams on design, development and testing of software programs or ML algorithms

About Demandbase:

Demandbase is the Smarter GTM™ company for B2B brands. We help marketing and sales teams overcome disruptive data and technology fragmentation, injecting Account Intelligence into every step of the buyer journey. We empower clients to orchestrate actions seamlessly across advertising, account-based experiences, and sales motions allowing them to spot opportunities earlier, engage intelligently, and close deals faster. We are deeply committed to employee growth, innovation, and diversity. With offices in San Francisco Bay Area, New York, Seattle, and teams in the UK and India, Demandbase fosters an inclusive environment, recognized consistently as one of the best workplaces.

Role

We are looking for a Staff Machine Learning Engineer to raise the bar for AI system design and ML engineering across our B2B SaaS platform. This is a senior IC role focused on building production-grade AI systems where ambiguity, context constraints, scalability, and operational correctness are first-order concerns. You will lead complex, cross-team initiatives while remaining hands-on in designing, building, and operating AI/ML systems.

Responsibilities

• Lead the design and development of AI/ML systems starting from problem framing, decision boundaries, and operational constraints.
• Drive system and architecture decisions for AI and ML platforms, ensuring scalability, performance, a

Company Description

Diligent Global, founded in 2010, is a premier global services & products organization with deep capabilities in SAP, Microsoft technologies, Product Development & Managed Services. The company serves enterprise clients worldwide in sectors such as Oil & Gas, Manufacturing, Retail, Life Sciences, and more. Know more about us on and follow us on LinkedIn.

Experience: 3–6 Years

Location: Chennai (Work from Office)

Availability to join: Immediate joiners preferred.

We are hiring Data Scientist to own and deliver production-grade ML solutions. This role combines hands-on machine learning with product ownership, focusing on real business impact rather than experimentation alone. You will work closely with engineering, product, and business teams to design, deploy, and scale AI-driven features.

Key Responsibilities:

Define and own ML use cases for connected vehicles, predictive maintenance, and rider analytics

Manage the end-to-end ML lifecycle: data preparation, modeling, deployment, monitoring

Collaborate with product and engineering teams to convert ML insights into production features

Monitor model performance, drift, and retraining needs

Support pilots, A/B testing, and adoption measurement

Communicate ML outcomes and KPIs to stakeholders

Required Skills:

3–6 years

Head of Machine Learning — Voxela (India)

About Voxela

Voxela is building AI-powered vision systems that act as a virtual caregiver for senior living facilities, hospitals, and care organizations. Our platform analyzes real-time video to detect safety events, generate insights, and assist caregivers proactively. We are a fast-growing startup operating globally, combining edge AI, computer vision, and intelligent automation to transform care delivery.

Role Overview

We are looking for a Head of Machine Learning to lead Voxela’s AI vision roadmap and build a world-class ML organization. This is a hands-on leadership role where you will define technical direction, architect scalable ML systems, and mentor a high-performance team. You will work closely with founders, product, and engineering to ship production-grade AI systems deployed across real environments.

This role is ideal for someone who thrives in fast-paced startup environments and wants to build category-defining AI products from the ground up.

Responsibilities

• Define and execute Voxela’s ML strategy across computer vision, multimodal AI, and edge intelligence
• Architect and scale production ML pipelines from data collection → training → deployment → monitoring
• Lead design and optimization of real-time inference systems running on edge devices and cloud infrastructure
• Own model accu

Responsibilities:

• Develop Python applications and software solutions based on business requirements and technical specifications, with a focus on integrating AI/ML capabilities.
• Write well-designed, efficient, and reusable code adhering to best practices and coding standards.
• Participate in code reviews to ensure adherence to coding standards, maintainability, and scalability.
• Debug and troubleshoot issues in existing software applications, providing timely resolutions.
• Optimize application performance and ensure scalability by implementing efficient algorithms and data structures, especially for AI/ML models.
• Integrate Python applications with databases, APIs, and other third-party services as required, including data pipelines for AI/ML processes.
• Stay updated with the latest industry trends, tools, and technologies related to Python development and AI/ML applications.
• Continuously improve software development processes and practices to enhance productivity and quality.
• Collaborate with cross-functional teams, including product managers, designers, and quality assurance engineers, to ensure timely delivery of high-quality software products.
• Continuously discover, evaluate, and implement new technologies.
• Test products in controlled, real situations before going live, ensuring AI/ML functionalities perform as expected.
• Design, initiate, and handle

We are seeking a AI/ML Engineer with a strong technical foundation and proven technical leadership experience to drive the development and deployment of ML and GenAI-powered solutions in a cloud-native environment.

In this role, you will work with a high-impact team of AI/ML, Data and MLOps engineers in a matrixed org structure , collaborate cross-functionally with engineering, product, and DevOps, and own strategic initiatives focused on intelligent workload management, cloud cost optimization, and infrastructure automation.

If you are passionate about building scalable ML systems, driving team performance, and bringing cutting-edge AI (including LLMs and Agentic AI) into production, this is your opportunity to make a meaningful impact.

Company Description

Pandita AI Inc is a leader in machine learning innovation, creating products that revolutionize how industries and individuals leverage data and AI technologies. Our solutions are recognized for their excellence and forward-thinking approach, establishing us as a trusted partner across multiple sectors. We are dedicated to advancing the field of AI and fostering impactful transformations in the technological landscape. At Pandita AI Inc, we value collaboration, innovation, and delivering meaningful results.

Role Description

This is a full-time on-site role located in Mysore. The Sr. Machine Learning Scientist will engage in designing and implementing cutting-edge machine learning algorithms, conducting advanced research in artificial intelligence, and developing novel solutions for complex problems. Responsibilities include working with large-scale datasets, experimenting with neural networks, and contributing to the creation of impactful AI-driven products. Collaboration with cross-functional teams to ensure successful project execution is also a key aspect of this role.

Qualifications

• Strong expertise in Machine Learning, including Pattern Recognition and Neural Networks
• Solid foundation in Computer Science and Statistics
• Proficiency in Programming with Python. Knowledge of using claude, streamlit.
• Outstanding analytical and problem-solving skill

Title: Senior Machine Learning Engineer

Location: Vaishnavi Signature, Bellandur, Bengaluru( hybrid2 days onsite a week)

Full time

What You Will Achieve and Key Responsibilities

Research, Design, Develop and Deploy AI models and systems

• Lead the research and development of AI models - a varied portfolio ranging from small classifiers to fine-tuning LLM’s for specific use-cases
• Design, implement and deploy AI-based solutions to solve business and product problems
• Develop and implement strategies to track and improve the performance and efficiency of existing and new AI models and systems
• Operationalize efficient dataset creation and management
• Execute best practices for end-to-end data and AI pipelines
• Work closely with the leadership team on research and development efforts to explore cutting-edge technologies.
• Collaborate with cross-functional teams including full-stack engineers, product managers, QA engineers, data annotation experts, SMEs and other stakeholders to ensure successful implementation of AI technologies

Build and Mentor the AI Team

• Work closely with the AI & Engineering Leadership to support hiring of top AI talent
• Uphold our culture of engineering excellence by maintaining high standards in innovation & execution

MLOps / LLMOps Engineer

Remote

Fulltime Permanent Role

Exp. - 5+ Years

Max Salary Range 25 LPA

Job Summary

As an MLOps / LLMOps Engineer, you will design, automate, and operate scalable ML and

LLM systems on our client enterprise Lakehouse platform.

You will work closely with Data Science, Engineering, and Product teams to deploy reliable,

secure, and production-ready ML and GenAI solutions. This role focuses on operationalizing

ML models, building CI/CD pipelines, ensuring governance and compliance, and maintaining

high-performance, observable AI systems.

Primary Responsibility

ML/LLM Platform Development

- Operationalize model training, evaluation, packaging, and deployment using

Databricks, Delta Lake, and medallion architecture.

- Implement Unity Catalog model governance, lineage tracking, and access control.

- Develop reusable job templates, cluster policies, and standardized deployment

patterns.

ML/LLM Production Deployment

- Deploy and manage ML and GenAI solutions including risk scoring, anomaly detection,

predictive maintenance, NLP, and RAG pipelines.

- Build and optimize LLM pipelines using vector databases, model serving endpoints,

and inference workflows.

- Optimize models using quantization,

As an ML Engineer in the team, you will work closely with Data Science, Engineering, Platform, Product and Operations teams to build state-of- the-art ML based solutions for B2B SaaS products. This will entail applying advanced ML algorithms at scale for core products and developing robust end to end production pipelines which includes Human-in-the-Loop component to boost the quality.

The ideal candidate will have a strong background in machine learning model development, deploying large scale, high throughput machine learning pipelines to production, experience with developing and managing frameworks for machine learning platforms, which they can utilise to manage and improve our company’s AI/ML initiatives.

• Contribute to development of multiple AI driven end to end pipelines that allows for deployment and scalability of machine learning models
• Build an end-to-end machine learning platform, covering all lifecycle stages of a model, to ease model development and deployment
• Build tools and capabilities that help with data ingestion to feature engineering, data management and organisation
• Deploy cutting edge algorithms like LLMs etc. on GPUs along with distributed computing for scalability
• Contribute to tools and capabilities for model management and model performance monitoring
• Implement the best engineering practices for scaling ML-powered features, with a goal to enable the fast iteration of and ef

About the Company : Tundra Technical Solutions is among North America's leading providers of Information Technology, Engineering staffing and consulting services. Our success and our clients' success are built on a foundation of service excellence. Rather than continually trying to sell to new clients and companies and simply filling databases with candidates, we focus on developing stronger relationships and deeper knowledge of our existing clients' challenges and opportunities.

Tundra is a global provider of innovative staffing solutions and services. Unlike any other agency, we work as a strategic partner to the client and candidate, ever-evolving and leveraging OUR expertise to understand your needs and solve them, so you can focus on YOUR expertise.

Tundra operates with a straightforward Vision – “To be the number one representative of choice for top talent across the globe”. Tundra understands and believes that simply “finding people” is not adequate. Our clients deserve the best; the right people at theright time and for the right role.

Tundra’s proven ability to attract, engage and maintain relationships with the most talented resources worldwide is the fuel that empowers this success. Tundra values the talent we work with, and we are invested in creating meaningful relationships, we respect your time and experience and accredit OUR success to YOURS.

We’re Hiring: Senior D

What you’ll do:

• Develop, train, and validate machine learning models for regression, classification, clustering, and anomaly detection.
• Work with time-series and multivariate sensor data for predictive maintenance, machine health monitoring, and operational analytics.
• Implement feature engineering pipelines, including statistical features, rolling-window features, and domain-specific transformations.
• Apply probabilistic and statistical methods such as Bayesian inference, uncertainty estimation, and Monte Carlo simulation where applicable.
• Support financial and operational analytics models, including cost drivers, efficiency metrics, and performance trend analysis.
• Deep Learning, Vision & Generative AI
• Build and fine-tune deep learning models using PyTorch or TensorFlow (CNNs, sequence models, autoencoders).
• Develop computer vision solutions for visual inspection, defect detection, image-based classification, and pattern recognition in manufacturing and operational contexts.
• Work with image and video data pipelines, including preprocessing, augmentation, and model evaluation.
• Develop Generative AI solutions using LLMs for document summarization, insight generation, knowledge retrieval (RAG), and conversational interfaces.
• Implement embedding-based semantic search and similarity matching for unstructured and semi-structured data.
• Design and integ

Scribie is an AI-powered, Human Verified audio and video transcription service, trusted globally since 2008. We specialize in delivering accurate and reliable transcription solutions by blending advanced AI technology with human expertise. Headquartered in the US, we operate with a hybrid model in our Bangalore office, combining the flexibility of remote work with the collaboration of in-person engagement. This approach offers our team both autonomy and growth opportunities in a dynamic and supportive environment.

We’re building production-grade audio foundation models for high-stakes legal and enterprise transcription — real customer data, messy audio, real consequences.

This is not a paper-only research role.

You’ll own the full ML lifecycle:

• Fine-tuning large audio / multimodal models using SFT, LoRA, and RL-based preference optimization (DPO / PPO / ORPO)
• Beating strong baselines like Whisper-large, GPT-4o, Gemini, Claude on domain-specific data
• Designing WER, diarization, and alignment-driven evaluation stacks
• Taking models from research notebooks → production inference services
• Running daily experiments that directly impact quality, cost, and customer satisfaction

You’ll be our first ML hire, with real ownership over the audio ML roadmap — not a side project, not a support role.

📍 Bangalore (primarily

Position: AI / Machine Learning Engineer – Intelligent Automation

Experience: 5+ Years

Location: Ahmedabad, Gujarat

Company: MSBC Group

MSBC Group is seeking an experienced AI / Machine Learning Engineer to design and deliver enterprise-grade AI agents and intelligent automation solutions that enhance software engineering productivity. This role focuses on building scalable, production-ready AI systems used across development, QA, and DevOps functions.

The position offers an opportunity to work at the intersection of artificial intelligence, workflow automation, and software engineering , contributing to solutions with measurable business and operational impact.

Key Responsibilities

• Design and develop AI agents to automate software engineering and operational workflows
• Analyze existing engineering processes to identify and implement automation opportunities
• Build ML- and LLM-based solutions for task planning, reasoning, and decision support
• Integrate AI capabilities into developer tools and internal platforms
• Deploy, monitor, and continuously optimize AI systems in production environments
• Collaborate with engineering and product teams to ensure alignment with business objectives

Required Skills:

<

About Us

We are building an AI-native platform focused on intelligent automation and privacy-aware AI workflows. Our systems combine LLM reasoning, agent orchestration, multimodal understanding, and structured ML techniques to enable reliable enterprise AI execution at scale.

About the Role

We are looking for a senior ML Lead with strong hands-on experience in Agentic AI systems, LLM orchestration, and autonomous workflow design.

This is a deeply technical, hands-on leadership role. You will design and build autonomous AI engines capable of executing complex tasks with minimal to zero manual intervention, while ensuring reliability, determinism, and performance at scale.

We value strong engineering fundamentals (6+ years) along with 2+ years of production experience working with LLMs and agent-based systems.

What You’ll Build

· Advanced prompt engineering systems enabling LLMs to:

o Perform complex multi-step reasoning

o Strictly adhere to defined output schemas

o Execute structured decision logic

· Fully autonomous agent systems capable of completing tasks with zero manual intervention

· Dynamic system-prompt construction engines that adapt prompts in real-time based on user input and context

· Multi-agent orchestration frameworks with:

o Tool calling

o Memory management

o Reasoning chains <

About Us

We are building an AI-native platform focused on intelligent automation and privacy-aware AI workflows. Our systems combine LLM reasoning, agent orchestration, multimodal understanding, and structured ML techniques to enable reliable enterprise AI execution at scale.

About the Role

We are looking for a senior ML Lead with strong hands-on experience in Agentic AI systems, LLM orchestration, and autonomous workflow design.

This is a deeply technical, hands-on leadership role. You will design and build autonomous AI engines capable of executing complex tasks with minimal to zero manual intervention, while ensuring reliability, determinism, and performance at scale.

We value strong engineering fundamentals (6+ years) along with 2+ years of production experience working with LLMs and agent-based systems.

What You’ll Build

· Advanced prompt engineering systems enabling LLMs to:

o Perform complex multi-step reasoning

o Strictly adhere to defined output schemas

o Execute structured decision logic

· Fully autonomous agent systems capable of completing tasks with zero manual intervention

· Dynamic system-prompt construction engines that adapt prompts in real-time based on user input and context

· Multi-agent orchestration frameworks with:

o Tool calling

o Memory management

o Reasoning chains <

Job Description

We are looking for a skilled Generative AI Developer to join our Controls Technology team

and contribute to the development and integration of innovative generative AI solutions. In

this role, you’ll work closely with senior developers, AI architects, and business

stakeholders to deliver impactful AI-driven features and products.

Key Responsibilities:

- Implement and fine-tune generative AI models under the guidance of senior team

members.

- Contribute to prompt engineering and development of AI-powered workflows.

- Assist with the deployment, monitoring, and maintenance of AI models in

production environments.

- Collaborate with data scientists and engineers to ensure seamless integration of AI

capabilities.

- Perform data preprocessing, feature engineering, and API development for AI

applications.

- Participate in code reviews, testing, and documentation to ensure quality and

reliability.

- Stay updated with advancements in GenAI and share relevant learnings with the

team.

Required Technical Skills:

- Hands-on experience with at least one major machine learning framework (PyTorch,

TensorFlow, Keras).

- Familiarity with LLMs, prompt engineering, and fine-tuning (e.G., LoRA, QLoRA).

- Exposure to RAG systems and basic knowledge of hybrid search techniques.

- Und

Machine Learning Engineer

Location: Hyderabad, India – (5 days/onsite)

Experience: 2–5 Years

Company: Circuitry.Ai

About Circuitry.Ai

Circuitry.Ai is an Enterprise AI SaaS company building advanced AI systems for the U.S. SLM sector.

• Production-grade RAG architectures
• Fine-tuned LLM systems
• Knowledge Graph–driven AI
• Agentic AI frameworks
• Trust-centric AI systems with explainability and evidence

Our mission is to solve the 'Trust in AI' problem for enterprises by combining LLMs, graph retrieval, structured data systems, and rigorous evaluation pipelines.

We build real, production-grade AI systems — not demos.

Important Note

This role requires real, hands-on ML experience with large structured datasets and end-to-end ML pipelines.

• Real production datasets
• End-to-end ML pipelines
• Model deployment experience

This role is not GenAI-focused. GenAI exposure is a plus, but not required.

Role Overview

We are seeking a Machine Learning Engineer with strong expertise in traditional ML algorithms and structured data systems.

• Structured data processing
• Feature engineering
• Model training & evaluation
• Deployment & automation

Mandatory

Job Responsibilities:

• Evaluate and source appropriate cloud infrastructure solutions for machine learning needs, ensuring cost-effectiveness and scalability based on project requirements.
• Automate and manage the deployment of machine learning models into production environments, ensuring version control for models and datasets using tools like Docker and Kubernetes.
• Set up monitoring tools to track model performance and data drift, conduct regular maintenance, and implement updates for production models.
• Work closely with data scientists, software engineers, and stakeholders to align on project goals, facilitate knowledge sharing, and communicate findings and updates to cross-functional teams.
• Design, implement, and maintain scalable ML infrastructure, optimizing cloud and on-premise resources for training and inference.
• Document ML processes, pipelines, and best practices while preparing reports on model performance, resource utilization, and system issues.
• Provide training and support for team members on ML Ops tools and methodologies, and stay updated on industry trends and emerging technologies.
• Diagnose and resolve issues related to model performance, infrastructure, and data quality, implementing solutions to enhance model robustness and reliability.

Education, Technical Skills & Other Critical Requirement:

• 6+ years of relev

SDE II – Machine Learning

Location: Bangalore

Experience: 3–6 years

About Media.Net

Media.Net is a global leader in ad tech, powering billions of ad impressions across web and mobile formats. Our proprietary contextual advertising and machine learning technologies serve major global publishers and advertisers.

We focus on building intelligent systems that solve complex optimization and targeting problems at scale. If you thrive in a fast-paced, data-driven environment and are excited to work on products that impact millions of users daily, Media.Net is the place for you.

Role Overview

As an SDE II – Machine Learning, you will be a core contributor in designing, building, and scaling ML-driven systems that power our real-time ad platforms. You'll be responsible for full-stack ML development—from data engineering and model development to scalable deployment—working closely with product, data science, and engineering teams.

What You'll Do

· Build and deploy machine learning models for ranking, bid optimization, and click-through rate prediction.

· Design scalable and fault-tolerant data pipelines and services that serve real-time and batch ML workloads.

· Work with large volumes of structured and unstructured data to extract meaningful patterns.

· Collaborate with data scientists to convert prototypes into produc

Why should you choose us?

Rakuten Symphony is reimagining telecom, changing supply chain norms and disrupting outmoded thinking that threatens the industry’s pursuit of rapid innovation and growth. Based on proven modern infrastructure practices, its open interface platforms make it possible to launch and operate advanced mobile services in a fraction of the time and cost of conventional approaches, with no compromise to network quality or security. Rakuten Symphony has operations in Japan, the United States, Singapore, India, South Korea, Europe, and the Middle East Africa region. For more information, visit:

Building on the technology Rakuten used to launch Japan’s newest mobile network, we are taking our mobile offering global.

To support our ambitions to provide an innovative cloud-native telco platform for our customers, Rakuten Symphony is looking to recruit and develop top talent from around the globe. We are looking for individuals to join our team across all functional areas of our business – from sales to engineering, support functions to product development.

Let’s build the future of mobile telecommunications together!

About Rakuten Group, Inc. (TSE: 4755) is a global leader in internet services that empower individuals, communities, businesses and society. Founded in Tokyo in 1997 as an online marketplace, Rakuten has expanded to offer services in e-commerce, fintech, digital content and communications to approximatel

Data Scientist

Who we are: -

At CitiusTech , we constantly strive to solve the industry's greatest challenges with technology, creativity, and agility. With over 8,500 healthcare technology professionals worldwide, CitiusTech powers healthcare digital innovation, business transformation, and industry-wide convergence for over 140 organizations through next-generation technologies, solutions, and products. We aim to accelerate the transition to a human-first, sustainable, and digital healthcare ecosystem with the world's leading Healthcare and life sciences organizations and our partners.

Here is an opportunity for you to make a difference and collaborate with global leaders to shape the future of healthcare and positively impact human lives.

Our vision: -

To inspire new possibilities for the health ecosystem with technology and human ingenuity.

What is in it for you?

As a Data Scientist , you will be a part of an Agile team to build healthcare applications and implement new features while adhering to the best coding development standards.

Responsibilities: -

• Develop advanced algorithms that solve problems of large dimensionality in a computationally efficient and statistically effective manner
• Implement statistical and Data Mining techniques e.G. Hypothesis Testing, Machine Learning, and Retrieval Processes on a large amount of data t

Dreaming big is in our DNA. It’s who we are as a company. It’s our culture. It’s our heritage. And more than ever, it’s our future. A future where we’re always looking forward. Always serving up new ways to meet life’s moments. A future where we keep dreaming bigger. We look for people with passion, talent, and curiosity, and provide them with the teammates, resources and opportunities to unleash their full potential. The power we create together – when we combine your strengths with ours – is unstoppable. Are you ready to join a team that dreams as big as you do?

AB InBev GCC was incorporated in 2014 as a strategic partner for Anheuser-Busch InBev. The center leverages the power of data and analytics to drive growth for critical business functions such as operations, finance, people, and technology. The teams are transforming Operations through Tech and Analytics.

Do You Dream Big?

We Need You.

Job Description

Job Title: Senior ML Engineer

Location: Bangalore (Onsite)

Reporting to: Senior Manager, Analytics

PURPOSE OF ROLE

As a Senior MLE, you will be responsible for driving the technical backbone of the internal ML platform—from model containerization and deployment at scale to architecting backend repos and pipelines that enable integration with the software/frontend layers. You will lead the end-to-

Dreaming big is in our DNA. It’s who we are as a company. It’s our culture. It’s our heritage. And more than ever, it’s our future. A future where we’re always looking forward. Always serving up new ways to meet life’s moments. A future where we keep dreaming bigger. We look for people with passion, talent, and curiosity, and provide them with the teammates, resources and opportunities to unleash their full potential. The power we create together – when we combine your strengths with ours – is unstoppable. Are you ready to join a team that dreams as big as you do?

AB InBev GCC was incorporated in 2014 as a strategic partner for Anheuser-Busch InBev. The center leverages the power of data and analytics to drive growth for critical business functions such as operations, finance, people, and technology. The teams are transforming Operations through Tech and Analytics.

Do You Dream Big?

We Need You.

Job Description

Job Title: Senior ML Engineer

Location: Bangalore (Onsite)

Reporting to: Senior Manager, Analytics

PURPOSE OF ROLE

As a Senior MLE, you will be responsible for driving the technical backbone of the internal ML platform—from model containerization and deployment at scale to architecting backend repos and pipelines that enable integration with the software/frontend layers. You will lead the end-to-

Job Title: ML Engineer

Location: Noida

Experience Required: 3–6 years

About the Job

We are looking for a Machine Learning Engineer to design, build, and deploy ML models that power intelligent agent systems and enterprise AI solutions.

The Machine Learning Engineer will be responsible for developing scalable ML pipelines, optimizing models for performance, and ensuring robust deployment in production environments. This role requires strong technical expertise in ML algorithms and practical experience in MLOps.

Responsibilities

Hands-on experience in deploying ML models at scale with automated monitoring and retraining in production environments.

Design and implement machine learning models for prediction, classification, and optimization tasks.

Build and maintain scalable ML pipelines for data ingestion, training, and deployment.

Collaborate with data scientists and engineers to integrate models into agent workflows.

Monitor and improve model performance post-deployment using automated retraining strategies.

Ensure compliance with best practices for security, reliability, and ethical AI.

Mandatory Skills

Proficiency in Python , TensorFlow , and PyTorch .

Experience with ML pipelines and MLOps tools (MLflow, Kubeflow).

St

Key Responsibilities

• Model Implementation: Take ownership of Large Language Models (LLM) and Small Language Models (SLM).
• Python Development: Build and maintain high-performance solutions using a custom Python stack.
• System Engineering: Execute complex tool chaining and develop sophisticated system prompts to optimize AI output.
• Performance Excellence: Contribute to a project that offers potential for long-term client absorption based on individual performance after the initial six-month period.

Required Skills & Expertise

• AI/ML: Direct experience with LLMs and SLMs.
• Programming: Advanced proficiency in custom stack Python development.
• Orchestration: Hands-on experience with tool chaining and system prompt engineering.
• Domain: Interest or experience in financial technology and capital markets

Solution Architect - MLOps

Who we are: -

At CitiusTech , we constantly strive to solve the industry's greatest challenges with technology, creativity, and agility. With over 8,500 healthcare technology professionals worldwide, CitiusTech powers healthcare digital innovation, business transformation, and industry-wide convergence for over 140 organizations through next-generation technologies, solutions, and products. We aim to accelerate the transition to a human-first, sustainable, and digital healthcare ecosystem with the world's leading Healthcare and life sciences organizations and our partners.

Here is an opportunity for you to make a difference and collaborate with global leaders to shape the future of healthcare and positively impact human lives.

Our vision: -

To inspire new possibilities for the health ecosystem with technology and human ingenuity.

What is in it for you?

As Solution Architect - MLOps, you will be a part of an Agile team to build healthcare applications and implement new features while adhering to the best coding development standards.

Responsibilities: -

• Monitoring Build & Production systems using automated monitoring and alarm tools
• Creating and using benchmarks, metrics, and monitoring to measure and improve services.
• Providing best practices and executing POC for automated and efficient model operation

The AI Specialist is responsible for architecting and operating an end-to-end data and AI platform, including a centralised data warehouse, ETL/ELT pipelines, ML systems, and LLM-based applications. The role involves building RAG architectures with vector databases and embedding pipelines, applying MLOps and LLMOps practices, and orchestrating data and AI workflows using n8n for production-scale deployment.

Key Responsibilities:

Data Platform & Engineering

• Design, build, and maintain a centralised data warehouse to support analytics, ML, and Large Language Model (LLM) workloads
• Develop and manage ETL/ELT pipelines to ingest, transform, and curate data from internal and external sources
• Ensure data quality, reliability, security, and governance across all data systems

Machine Learning & LLM Development

• Design, train, evaluate, deploy, and maintain machine learning models for predictive and analytical use cases
• Develop and integrate LLM solutions for enterprise and business applications
• Design and implement Retrieval-Augmented Generation (RAG) pipelines using structured and unstructured data
• Build and manage vector databases and embedding pipelines for semantic search and RAG systems

MLOps, LLMOps & Automation

• Deploy and maintain ML and LLM services in production environments
• M

Job Title: Senior Software Development Engineer (Machine learning)

About Media.Net:

Media.Net is a leading online ad-tech company that develops innovative products for advertisers and publishers. Media.Net has one of the most comprehensive portfolios of advertising technology in the industry across search, native, display, mobile, local, products and video. Media.Net manages high-quality ad supply on over 500,000 websites and its platform and products are licensed by some of the largest publishers, ad networks and other ad tech companies worldwide.

Media.Net is one of the largest contextual advertising company worldwide. Media.Net has key operation centers across – North America, Europe and Asia. Media.Net's US HQ is based in New York, and Global HQ in Dubai.

We are a cutting-edge AdTech company solving the industry's most complex challenges end-to-end — from real-time bidding infrastructure to ad performance optimization. We harness the power of behavioral and contextual data to drive precision and performance at scale.

What You’ll Do

• Design and implement state-of-the-art Generative AI models and predictive machine learning pipelines tailored to AdTech applications.
• Analyze and process large-scale datasets to extract insights and power intelligent decision-making in real-time environments.
• Build and maintain scalable ML systems that integra

Dreaming big is in our DNA. It’s who we are as a company. It’s our culture. It’s our heritage. And more than ever, it’s our future. A future where we’re always looking forward. Always serving up new ways to meet life’s moments. A future where we keep dreaming bigger. We look for people with passion, talent, and curiosity, and provide them with the teammates, resources and opportunities to unleash their full potential. The power we create together – when we combine your strengths with ours – is unstoppable. Are you ready to join a team that dreams as big as you do?

AB InBev GCC was incorporated in 2014 as a strategic partner for Anheuser-Busch InBev. The centre leverages the power of data and analytics to drive growth for critical business functions such as operations, finance, people, and technology. The teams are transforming Operations through Tech and Analytics.

Do You Dream Big?

We Need You.

Job Description

Job Title: Machine Learning Engineer

Location: Bangalore (Onsite)

Reporting to: Sr. Manager - Analytics

PURPOSE OF ROLE

We are seeking a skilled and motivated Machine Learning Engineer to design, build, and deploy ML systems that power intelligent products and services. In this role, you will collaborate closely with data scientists, engineers, and product teams to deliver scalable and efficient solutions in production env

Work Location: Hyderabad

Experience: 15+ Years

Notice: Immedaite- 15 Days

About SIDGS:

SIDGS is a premium global systems integrator and global implementation partner of Google corporation, providing Digital Solutions & Services to Fortune 500 companies. Our Digital solutions go across following domains: User Experience, CMS, API Management, Microservices, DevOps, Cloud, Service Mesh, Artificial Intelligence, RPA domains.

We create innovative solutions in Digital, API Management, Cloud and DevOps space in partnership with Google. We understand that every business has a unique set of challenges and opportunities, and we leverage our unique industry insights, honed through decades of combined experience in the technology sector, to deliver the products, solutions, and services necessary to achieve best customer satisfaction and delivering positive impact to the communities.

Job Overview:

We are seeking a seasoned AIML practice headwho combines deep hands-on expertise in Python and machine learning with strong pre-sales solutioning skills . This role requires an architect who can engage clients, design scalable AI/ML solutions, and also lead or contribute to technical implementation.

Key Responsibilities:

🔹 AI/ML Architecture & Hands-on Delivery

Position – Senior Data Scientist ( Computer Vision ) - Immediate joiners only

Total Exp -6Years

Experience of working with computer vision /Python / R / SQL / NoSql

Must have experience with NLP / ML / AI

Based on data Set, recommend the right data science modelling technique to be used for solving business problem .Experience in generating data insight using model like Forecasting / Prediction / etc, Experience of working with some ELT tool / data collection method used.

Critical thinking skill with data visualization layer / ideate / brainstorm.

If interested kindly share me your resume at clarita.fernandes@sperton.com or give me a call at 9768928580

Total exp

Exp in Data Scientist

Exp in Computer Vision

Current ctc

Expected ctc

Notice period

Current Location

REgards,

Clarita

Our Company:

A fast-growing start-up headquartered in San Francisco, CA, USA in the heart of Silicon Valley. We recruit worldwide as our customer base is global. Reliable ground transportation provider, any type of vehicle globally.

Vision: Reliable ground transportation services globally with all types of vehicles.

Mission: Empower high-quality local fleets

Corporate Culture: team first

• Partner-centric
• Team collaboration,
• Never "not my job", end-to-end ownership,
• Continuous learning and improvement,
• Hard-working and pragmatic,
• Don’t be a middleman,
• Result-driven

Job title: Sr Machine Learning Engineer

Job description

• Develop and implement machine learning models using Python, SQL, and other relevant tools.
• Collaborate with cross-functional teams to identify opportunities for applying machine learning techniques to business problems.
• Conduct research and experimentation to explore new machine learning approaches and technologies.
• Optimize existing machine learning models for improved performance and efficiency.
• Provide technical leadership and mentorship to junior members of the team.

Job requirements

• Strong background in machine learning, natural language processing, artificial intelligence,

Role & Responsibilities

• Design and implement classic ML models (XGBoost, SVMs, clustering, etc.) for structured data use cases.
• Build and optimize deep learning pipelines using PyTorch/TensorFlow, with a focus on NLP and multimodal tasks.
• Develop modern AI systems , including RAG-based applications leveraging LangChain, LlamaIndex, and vector databases (FAISS, Weaviate).
• Architect and experiment with agentic AI systems featuring tool usage, memory, planning, and self-reflection (CrewAI, LangGraph, AutoGen).
• Deploy ML models in production environments using Docker, MLflow, Ray, or Kubernetes.

Preferred Candidate Profile

• 3–5 years of experience in software or ML engineering.
• Strong proficiency in Python , with hands-on experience in ML model building.
• Exposure to open-source LLMs (e.G., LLaMA, Mistral, Falcon).
• Bonus: Experience in fine-tuning LLMs or building autonomous AI agents .

Key Attributes

• Strong problem-solving and analytical skills.
• Ability to work collaboratively in fast-paced, research-driven environments.
• Passion for exploring cutting-edge AI technologies and applying them to real-world problems.

#MachineLearningEngineer #AIJobs #DeepLearning #NLP #GenerativeAI #LangChain #LL

Role & Responsibilities

• Design and implement classic ML models (XGBoost, SVMs, clustering, etc.) for structured data use cases.
• Build and optimize deep learning pipelines using PyTorch/TensorFlow, with a focus on NLP and multimodal tasks.
• Develop modern AI systems , including RAG-based applications leveraging LangChain, LlamaIndex, and vector databases (FAISS, Weaviate).
• Architect and experiment with agentic AI systems featuring tool usage, memory, planning, and self-reflection (CrewAI, LangGraph, AutoGen).
• Deploy ML models in production environments using Docker, MLflow, Ray, or Kubernetes.

Preferred Candidate Profile

• 3–5 years of experience in software or ML engineering.
• Strong proficiency in Python , with hands-on experience in ML model building.
• Exposure to open-source LLMs (e.G., LLaMA, Mistral, Falcon).
• Bonus: Experience in fine-tuning LLMs or building autonomous AI agents .

Key Attributes

• Strong problem-solving and analytical skills.
• Ability to work collaboratively in fast-paced, research-driven environments.
• Passion for exploring cutting-edge AI technologies and applying them to real-world problems.

#MachineLearningEngineer #AIJobs #DeepLearning #NLP #GenerativeAI #LangChain #LL

Mente Consultancies Pvt. Ltd. is looking for experienced Machine Learning and Programming Analysts at their office located in New Delhi. This is an on-site role.

The incumbent is expected to do the following: -

• Understanding the business problem and effectively devise data-driven solutions to solve the problems identified
• Work on programming assignments leveraging Python, R or SAS
• Work on design, development and testing of software programs or ML algorithms
• Work in a collaborative team environment under the guidance of a project leader and deliver the tasks assigned
• Be a self-starter and take the initiative for learning new technologies, algorithms through research etc.
• Be able to communicate effectively with the clients through oral and written mediums (power point, word) and interact with junior client stakeholders
• Use data and machine learning to deliver solutions for the clients

The incumbent should possess the below: -

• Graduation in any Engineering stream / MCA
• Worked on Programming languages. Knowledge of Python, R or SAS is a must
• Strong problem-solving skills
• Ability to work towards end product
• Should have cleared the UG/PG degree
• Relevant work experience of minimum 3 years

In this role, you'll own the definition and implementation of AI-driven innovation in our SEO team, leveraging ML, GenAI, LLMs, and/or NLP to transform complex business requirements and customer needs into innovative technology solutions. Your expertise will be key in shaping data-driven strategies and addressing complex data challenges. With expertise in ML, AI, text analysis, text embedding, language modeling, and generation, you'll design and develop scalable AI-powered technology solutions, prioritize initiatives, drive data-driven insights, and deliver business impact. This role requires working with a cross-functional team, including scientists, engineers, and product management to develop scalable and maintainable AI solutions for both structured and unstructured data. The ideal candidate has strong technological skills in ML, LLMs, GenAI and NLP, excellent written documentation skills, and experience with big data technologies.

You should care deeply about our Clients and strive to deliver value through applied science. You should work well in a fast-paced setting, using your skills to create effective solutions. With a combination of deep business know-how and hands-on skills, you will tackle complex challenges and drive continuous improvement.

Key job responsibilities

• Develop innovative solutions to complex problems using ML, LLMs
• Apply technical expertise to implement novel algorithms and modeling solutions, in

Job Title: Instructor Offline Learning Centre

Location: Panipat, Haryana

Working Days: 6 (Monday to Saturday)

About upGrad

Founded in 2015, upGrad is Asias largest integrated Learning, Skilling, Workforce Development, and Placement company. Its offerings include online and hybrid degrees, study abroad programs,

certifications, bootcamps, and doctorates for working professionals. Through its B2B arm, upGrad

also provides enterprise training, recruitment, and staffing services. With 10M+ learners across 100+countries, 300+ global university partners, 2500+ pathway connections, and 20,000+ recruitment partners, upGrad continues to empower careers worldwide.

Learn more:

About the Role

As an Instructor at our Offline Learning Centres, you will deliver engaging sessions in Data Science, Machine Learning, Deep Learning, and related fields to students and working professionals. You will play a vital role in ensuring high-quality learning outcomes through classroom teaching, mentorship, and academic support.

This is a unique opportunity to combine your passion for technology and education, while shaping the careers of tomorrow.

6 Reasons to Join upGrad Offline Learning Centres

1. Career Growth & SME Pathway: Accelerate your career with opportunities to move into leadership roles and grow as a Subject Matter Expert by contributi

Job Summary:

We are looking for a results-driven Senior AI/ML Engineer to lead the development and deployment of scalable machine learning models and intelligent systems. You will be at the forefront of building AI solutions that solve high-value business problems, with full ownership from data preparation to model monitoring. This is a key role in a hands-on, production-grade AI/ML team , working with state-of-the-art tooling and infrastructure.

Key Responsibilities:

• Design and implement robust, end-to-end ML models and AI pipelines to solve real-world business challenges.
• Build and maintain scalable data pipelines for structured and unstructured datasets — including data extraction, cleansing, feature engineering, and labeling.
• Train and tune ML models using modern frameworks such as TensorFlow, PyTorch , and scikit-learn .
• Deploy models to production using MLOps tools like MLflow, Kubeflow , or Amazon SageMaker .
• Collaborate with Product, Engineering, and Data Science teams to embed ML into customer-facing solutions .
• Monitor and optimise models for performance, reliability, and drift detection in live environments.
• Conduct R&D on cutting-edge techniques in LLMs, NLP, and computer vision , and apply them to production use cases.
• Bu

Head of Machine Learning — Voxela (India)

About Voxela

Voxela is building AI-powered vision systems that act as a virtual caregiver for senior living facilities, hospitals, and care organizations. Our platform analyzes real-time video to detect safety events, generate insights, and assist caregivers proactively. We are a fast-growing startup operating globally, combining edge AI, computer vision, and intelligent automation to transform care delivery.

Role Overview

We are looking for a Head of Machine Learning to lead Voxela’s AI vision roadmap and build a world-class ML organization. This is a hands-on leadership role where you will define technical direction, architect scalable ML systems, and mentor a high-performance team. You will work closely with founders, product, and engineering to ship production-grade AI systems deployed across real environments.

This role is ideal for someone who thrives in fast-paced startup environments and wants to build category-defining AI products from the ground up.

Responsibilities

• Define and execute Voxela’s ML strategy across computer vision, multimodal AI, and edge intelligence
• Architect and scale production ML pipelines from data collection → training → deployment → monitoring
• Lead design and optimization of real-time inference systems running on edge devices and cloud infrastructure
• Own model accu