Find Your Dream Machine Learning Job in India

Role: Machine Learning Engineering Lead

Location: Thane, Mumbai

Type: Full-Time opportunity

Exp level: 2+ Years

Required Skills & Experience Must Have

• 3+ years building ML/NLP evaluation pipelines in Python

• LLM evaluation techniques: LLM-as-judge, semantic similarity, embedding search, red-team dataset curation

• Experience designing abstraction layers for heterogeneous data sources (different APIs, formats, completeness levels)

• Adversarial robustness testing for LLMs (prompt injection, jailbreaking, information extraction)

• AWS services: ECS/Fargate, S3, RDS, SQS, CloudWatch

• Practical implementation of AI ethics, fairness, and bias metrics (not just awareness)

• CI/CD with security-integrated workflows (SAST, dependency scanning)

• Willingness to undergo BPSS-equivalent vetting Strong Preference

• Experience with Responsible AI / AI governance frameworks (NIST AI RMF, ISO 42001, EU AI Act tooling, UK CDEI)

• Government or regulated-sector compliance experience (data residency, audit trails, clearance constraints)

• LLM observability platforms (Langfuse, LangSmith, or similar)

• Multi-tenant or multi-supplier evaluation platform experience

• Published or presented on AI safety, alignment, or Responsible AI measurement

• Led or mentored a small ML team (2-5 people)

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 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 modelling.
• 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.

Cross-functional impact

• Collaborate with Designers, UX Researchers, Product Managers, and Software Engineers to integrate ML and GenAI-driven features into Glance's consumer experiences.
• Contribute to Glance's ML/AI thought leadership-blogs, case studies, internal tech talks, and industry conferences.
• Thrive in a multi-functional, highly collaborative team environment with engineering, product, business, and creative teams.
• Plus: Interface with stakeholders across Product, Business, Data, and Infrastructure to align ML initiatives with strategic priorities.

We are seeking candidates with deep expertise in ML, recommendation systems, and a strong appetite for building agentic AI systems.

You should have experience with:

• Large-scale ML and recommendation systems (collaborative filtering, ranking models, content-based approaches, embeddings).
• Classical ML and deep learning techniques across NLP, sequence modelling, RL, clustering, and time series.
• Experience in deploying ML workflows/models in production system
• Big data processing (Spark, distributed data systems) and cloud computing.
• Designing end-to-end ML solutions-from prototype to production.
• Plus: Building or experimenting with LLMs, generative models, and agentic AI workflows (e.g., autonomous evaluators, self-improving pipelines, automated experiment agents).

Qualifications

• Bachelor's/master's in computer science, Statistics, Mathematics, Electrical Engineering, Operations Research, Economics, Analytics, or related fields. PhD is a plus.
• 6+ years of industry experience in ML/Data Science, ideally in large-scale recommendation systems or personalization.
• Experience with LLMs, retrieval systems, generative models, or agentic/autonomous ML systems is highly desirable.
• Expertise with algorithms in NLP, Reinforcement Learning, Time Series, and Deep Learning, applied on real-world datasets.
• Proficient in Python and comfortable with statistical tools (R, NumPy, SciPy, PyTorch/TensorFlow, etc.).
• Strong experience with the big data ecosystem (Spark, Hadoop) and cloud platforms (Azure, AWS, GCP/Vertex AI).
• Comfortable working in cross-functional teams.
• Familiarity with privacy-preserving ML and identity-less ecosystems (especially on iOS and Android).
• Excellent communication skills with the ability to simplify complex technical concepts.

We value curiosity, problem-solving ability, and a strong bias toward experimentation and production impact.

Our team includes engineers, physicists, economists, mathematicians, and social scientists-a great data scientist can come from anywhere.

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.

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.

We Are a Match Because You Have:

• 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).

Job Profile: LLM Observability Engineer

Location - Bhubaneshwar

Job Description:

We are looking for a skilled LLM Observability Engineer to join our team and ensure optimal performance, reliability, and cost-efficiency of our large language model (LLM) applications. You will be instrumental in designing performance tests, implementing observability practices, and providing insights to enhance model quality and system robustness.

Key Responsibilities:

Must Have

• Design and Execute Performance Tests: Develop and implement comprehensive test plans and scripts to evaluate the performance, scalability, and stability of LLM applications under various loads.
• Implement LLM Observability: Instrument LLM applications to capture rich telemetry data, including prompts, responses, token usage, latency, and error information, using specialized tools and frameworks like Datadog, LangChain, or OpenTelemetry.
• Monitor and Analyze Metrics: Track key performance indicators (KPIs) such as response time, throughput, cost per query, accuracy, and resource utilization using real-time dashboards and monitoring systems.
• Identify and Mitigate Bottlenecks: Analyze performance test results and production data to pinpoint performance bottlenecks, errors, and potential issues (e.g., high latency in RAG pipelines) and collaborate with development teams on optimization.
• Conduct Automated Evaluations: Implement automated quality checks and evaluations (e.g., hallucination detection, toxicity classifiers, relevance scoring) to continuously assess model output quality.
• Strong knowledge of performance testing methodologies and load testing tools such as JMeter, LoadRunner, or Gatling. Familiarity with the unique challenges of LLMs, including non-determinism, hallucinations, and prompt sensitivity.
• Experience with LLM observability platforms and tools (e.g., Datadog LLM Observability, Arize AI, Langfuse) is highly desirable. Proficiency in programming/scripting languages (e.g., Python, Java).

Nice to have

• Troubleshoot Production Issues: Utilize tracing and logging data to quickly diagnose the root cause of issues in complex LLM workflows and agentic applications.
• Ensure Security and Compliance: Monitor model behavior for potential security risks, such as prompt injections or sensitive data leaks, and ensure compliance with data protection regulations.
• Optimize Costs: Track and manage token usage and computational resource consumption to ensure cost-effectiveness and alert teams to potential budget overruns.
• Collaborate and Report: Work closely with data scientists, ML engineers, and QA teams to provide actionable insights and recommendations for model fine-tuning and system architecture improvements.
• Excellent analytical problem-solving and communication skills.

Experience Required:

• 4 positions (2-8 years' experience)

Glance - An InMobi Group Company

Founded in 2019, Glance is a consumer technology company that operates disruptive digital platforms including Glance, Roposo, and Nostra. Glance's 'smart lock screen' inspires consumers to make the most of every moment by surfacing relevant experiences without the need for searching and downloading apps. Glance Lock Screen is currently available on over 450 million smartphones worldwide. Roposo is a LIVE platform that is revolutionizing live experiences, through a unique, immersive, creator-led approach. Nostra is the largest gaming platform in India and Southeast Asia, offering gamers engaging ways to discover, play, watch, learn and compete.

Headquartered in Singapore, Glance is an unconsolidated subsidiary of InMobi Group and is funded by Jio Platforms, Google, and Mithril Capital. For more information visit nostra.gg, and

What should you know about joining Glance?

At Glance, we walk the talk - free yourself, dream big, and chase your passion! On joining, you'll have opportunities to make an immediate impact on mission-critical projects, as you work with highly capable and ambitious peer groups.

Be rewarded for your autonomy even as you collaborate. Ideate, innovate, and inspire by leveraging bleeding-edge tech to disrupt consumer experiences.

While you work, we'll take care of nourishing your body, mind, and soul. This includes daily meals, gym, trainings, tech tools, and regular unwind sessions. Also, feel free to bring your kids - even the furry ones - to the office!

What You'll Do

As a Data Scientist - Recommendation Systems, you will own and drive high-impact ML systems powering personalization across Glance products.

• Design, build, and deploy large-scale recommendation and personalization models using diverse, high-volume data sources.
• Lead rapid experimentation-from hypothesis formulation to offline evaluation and online A/B testing-balancing speed, rigor, and business impact.
• Develop and productionize end-to-end ML pipelines, including data preparation, feature engineering, model training, evaluation, and monitoring.
• Partner closely with Product, Engineering, Design, UX, and Business teams to translate product goals into scalable ML solutions.
• Monitor model health and performance, applying statistical techniques to ensure robustness and long-term effectiveness.
• Explore and prototype new ML techniques to improve relevance, engagement, and monetization.
• Act as a technical interface for stakeholders, clearly articulating trade-offs, results, and next steps.
• Contribute to Glance's thought leadership through blogs, case studies, and industry conference talks.

What We're Looking For

Core Expectations

• Deep expertise in Machine Learning, Data Science, and Recommendation Systems at scale.
• Strong applied understanding of experimentation, metrics, and causal reasoning in real-world systems.
• Ability to take models from idea prototype production business impact.

Experience & Skills

• 10+ years of industry experience in ML/Data Science building large-scale recommendation or personalization systems.
• Hands-on experience applying techniques from ML, Deep Learning, NLP, Reinforcement Learning, Time Series, and Statistics.
• Strong programming skills in Python with production-quality code practices.
• Experience with big data ecosystems, especially Apache Spark.
• Familiarity with cloud platforms such as AWS, GCP (Vertex AI), or Azure.
• Experience operating in identity-constrained / privacy-aware environments (e.g., iOS/Android, identity-less systems) is a plus.
• Excellent communication skills-able to explain complex technical ideas clearly to non-technical stakeholders.
• High curiosity, strong problem-solving ability, and a bias toward learning and experimentation.

Qualifications

• Bachelor's or Master's degree in a quantitative field such as Computer Science, Electrical Engineering, Statistics, Mathematics, Operations Research, Economics, Analytics, or Data Science.
• PhD is a plus, but not mandatory.
• We value diverse academic backgrounds-great data scientists come from many disciplines.

Job Profile: LLM Observability Engineer

Location - Bhubaneshwar

Job Description:

We are looking for a skilled LLM Observability Engineer to join our team and ensure optimal performance, reliability, and cost-efficiency of our large language model (LLM) applications. You will be instrumental in designing performance tests, implementing observability practices, and providing insights to enhance model quality and system robustness.

Key Responsibilities:

Must Have

• Design and Execute Performance Tests: Develop and implement comprehensive test plans and scripts to evaluate the performance, scalability, and stability of LLM applications under various loads.
• Implement LLM Observability: Instrument LLM applications to capture rich telemetry data, including prompts, responses, token usage, latency, and error information, using specialized tools and frameworks like Datadog, LangChain, or OpenTelemetry.
• Monitor and Analyze Metrics: Track key performance indicators (KPIs) such as response time, throughput, cost per query, accuracy, and resource utilization using real-time dashboards and monitoring systems.
• Identify and Mitigate Bottlenecks: Analyze performance test results and production data to pinpoint performance bottlenecks, errors, and potential issues (e.g., high latency in RAG pipelines) and collaborate with development teams on optimization.
• Conduct Automated Evaluations: Implement automated quality checks and evaluations (e.g., hallucination detection, toxicity classifiers, relevance scoring) to continuously assess model output quality.
• Strong knowledge of performance testing methodologies and load testing tools such as JMeter, LoadRunner, or Gatling. Familiarity with the unique challenges of LLMs, including non-determinism, hallucinations, and prompt sensitivity.
• Experience with LLM observability platforms and tools (e.g., Datadog LLM Observability, Arize AI, Langfuse) is highly desirable. Proficiency in programming/scripting languages (e.g., Python, Java).

Nice to have

• Troubleshoot Production Issues: Utilize tracing and logging data to quickly diagnose the root cause of issues in complex LLM workflows and agentic applications.
• Ensure Security and Compliance: Monitor model behavior for potential security risks, such as prompt injections or sensitive data leaks, and ensure compliance with data protection regulations.
• Optimize Costs: Track and manage token usage and computational resource consumption to ensure cost-effectiveness and alert teams to potential budget overruns.
• Collaborate and Report: Work closely with data scientists, ML engineers, and QA teams to provide actionable insights and recommendations for model fine-tuning and system architecture improvements.
• Excellent analytical problem-solving and communication skills.

Experience Required:

• 4 positions (2-8 years' experience)

Job Title: MLOps Engineer.

Location: Meril - Merai Newage Pvt Ltd (NUVO AI Pvt Ltd ), D block, 4th floor, Kanagam Road, Tharamani, Chennai .

Shift Timings: Monday to Saturday (9 AM to 5.30 PM).

Experience: 3+ Years.

About the Role:

We are seeking an experienced MLOps Engineer with a focus on healthcare AI to join our innovative team. This role involves designing, implementing, and maintaining scalable MLOps pipelines for deploying deep learning models, including very deep vision models and large language models (LLMs), in clinical research, diagnostics, and healthcare analytics. The ideal candidate will have hands-on experience in healthcare AI environments, ensuring reliable, efficient, and compliant model deployments.

Key Responsibility:

Design and implement CI/CD pipelines for machine learning models using tools like Jenkins, GitHub Actions, or GitLab CI.

Deploy and manage very deep vision models (e.g., CNNs for medical imaging) and LLMs (e.g., for NLP in clinical notes) in production environments using Docker, Kubernetes, and cloud platforms (e.g., AWS, Azure, GCP).

Develop monitoring, logging, and alerting systems for ML models to ensure performance, drift detection, and retraining triggers using tools like Prometheus, Grafana, or MLflow.

Collaborate with data scientists, AI engineers, and clinicians to integrate models into healthcare workflows, handling large-scale medical datasets.

Implement version control for models and data using tools like DVC, MLflow, or Kubeflow.

Ensure compliance with healthcare regulations (e.g., HIPAA, GDPR) through secure data handling, model auditing, and privacy-preserving techniques.

Optimize model inference for efficiency, including model quantization, serving with TensorFlow Serving, TorchServe, or BentoML.

Troubleshoot deployment issues and perform A/B testing for model iterations.

Automate infrastructure as code using Terraform or Ansible for reproducible environments.

Must-Have Skills & Experience

Bachelor's or master's degree in computer science, Engineering, or a related field.

Minimum 3 years of experience as an MLOps Engineer or in a similar role.

Hands-on experience deploying very deep vision models (e.g., ResNet, U-Net for medical imaging) and LLMs (e.g., GPT variants, BERT for healthcare NLP).

Some experience in healthcare AI, such as working with medical imaging data (DICOM) or clinical datasets.

Proficiency in Python, containerization (Docker), orchestration (Kubernetes), and CI/CD.

Familiarity with ML frameworks like TensorFlow, PyTorch, and MLOps platforms like Kubeflow, SageMaker, or Vertex AI.

Strong understanding of cloud computing, DevOps practices, and version control (Git).

Knowledge of data security and regulatory compliance in healthcare.

Interested candidates can share their resume at:

Subject: MLOps Engineer - Medical Imaging.

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, and operational excellence.
• Design and evolve context engineering strategies for LLM-based systems under strict context window and latency constraints.
• Architect agent-based systems, including agent roles, interaction models, coordination strategies, and failure handling.
• Build and operate production AI systems with ownership of reliability, latency, cost, and correctness.
• Define evaluation frameworks for AI and agent behavior beyond offline model metrics.
• Scale AI-assisted development and reusable AI workflows to improve engineering velocity and system quality.
• Review and approve ML designs, guiding tradeoffs across model quality, system complexity, and business impact.
• Mentor senior engineers and data scientists, raising standards in AI system design, ML engineering, and production readiness.
• Partner with product and engineering leaders to translate ambiguous business problems into well-scoped, high-impact AI/ML solutions.
• Contribute to multi-quarter technical strategy, aligning AI and ML investments with company objectives.

Requirements

• 12+ years of experience across AI, ML, and software engineering roles.
• Strong coding skills in Python, Scala, or Java, with a track record of production-quality systems.
• Hands-on experience in building, evaluating, and operating LLM-powered systems in production.
• Strong understanding of context engineering, including information prioritization, relevance, and degradation under constraints.
• Experience designing AI workflows or agentic systems, including task decomposition, orchestration, and failure handling.
• Deep hands-on expertise in ML system design, NLP, feature engineering, and operational ML.
• Experience deploying AI/ML systems on at least one major cloud platform.
• Hands-on experience with Big Data or streaming systems (e.g., Spark, Kafka, Airflow).
• Proven ability to lead through influence, mentor engineers, and drive cross-team initiatives.
• Strong business and product mindset, with the ability to map AI/ML solutions to measurable outcomes.

Level Expectations

• Delivers multi-quarter AI/ML initiatives impacting multiple teams and products.
• Acts as a go-to expert for AI system design including context engineering, agent architectures, NLP, and ML system design.
• Raises the overall AI maturity of the organization through architecture, mentorship, and standards.

• Job Title: Machine Learning Engineer
• Company: Dataism Services
• Location: Viman Nagar, Pune, Maharashtra, India
• Experience: 4+ Years
• Employment Type: Full-Time

Role Overview

We are hiring a Machine Learning Engineer to own the path from trained model to production system. You will build inference pipelines, optimize model serving, integrate ML outputs into inspection planning dashboards, and ensure AI recommendations are reliable, explainable, and performant. You will also contribute to training infrastructure for fine-tuning and pretraining workflows on domain-specific data.

Key Responsibilities

• Build and maintain production ML inference pipelines with REST/gRPC APIs, model versioning, and automated validation gates.
• Optimize models for production deployment using quantization, distillation, mixed-precision inference, and efficient batching strategies.
• Design and implement data pipelines that feed training and inference workflows, handling industrial-scale data with NULL handling, schema inconsistencies, and cross-system joins.
• Deploy and manage cloud infrastructure for GPU workloads including instance selection (T4, A10G, A100), cost optimization, and capacity planning on AWS.
• Integrate LLM-powered features into production applications: RAG pipelines, embedding services, prompt orchestration, and token-cost management.
• Implement MLOps best practices: model registries, A/B testing, monitoring for drift and degradation, and automated retraining triggers.
• Build React + Node.js dashboards and tools that surface AI outputs to end users in inspection planning workflows.
• Collaborate with data scientists to productionize research models with minimal performance regression.

Essential Skills

• 4+ years of professional experience in ML Engineering, Backend Engineering, or MLOps.
• Strong software engineering fundamentals in Python with clean, testable, well-documented code. Experience with a systems language (Rust or C++) is a strong plus.
• Hands-on experience deploying ML models to production: Docker, CI/CD pipelines, REST API design, and model serving frameworks (TorchServe, Triton, or similar).
• Deep familiarity with the PyTorch ecosystem: model optimization, fine-tuning transformer architectures, debugging training issues, and mixed-precision workflows.
• Strong cloud infrastructure experience (AWS preferred): EC2 GPU instance selection, SageMaker endpoints, S3 data pipelines, Bedrock for managed LLM deployment, and cost-aware architecture.
• Proficiency with SQL Server and relational databases in production: data pipeline construction, NULL handling, VARCHAR(MAX) patterns, async fetch, and connection pooling.
• Experience with MLOps tooling: model registries, experiment tracking, monitoring for data drift and model degradation, and automated retraining.
• Working knowledge of LLM integration patterns: prompt engineering, RAG, embedding pipelines, fine-tuning workflows, and token-aware cost management.
• Familiarity with frontend technologies (React, Node.js) sufficient to build AI-powered dashboards and internal tools.
• Ability to work fast, independently, and efficiently in a startup-paced environment.

Preferred Qualifications

• Experience with Rust for backend services, ODBC integrations, or systems-level ML tooling.
• Familiarity with industrial inspection domains: PCMS, RBI (API 580/581), NDT methods, or petroleum refinery workflows.
• Experience with sensor data pipelines and IoT integration for condition monitoring or equipment health assessment.
• Knowledge of Azure infrastructure: VM provisioning, Event Grid monitoring, and GPU inferencing comparisons.

Job Type: Full-time

Experience: 5 + Years

Location: Chennai

Job Summary:

We are seeking a highly skilled and experienced Computer Vision Engineer to join our team. This role requires a minimum of 5 years of experience in the field of computer vision and a strong understanding of machine learning algorithms. The ideal candidate will have a proven track record of developing and implementing computer vision solutions in a real-world setting. This is a full-time, in-office position (Chennai) , and we are looking for someone who can start immediately.

Responsibilities:

Design and implement computer vision algorithms for object detection, tracking, and recognition.

Develop and optimize machine learning models for computer vision tasks.

Work with large datasets of images and videos.

Collaborate with other engineers and researchers to develop and deploy computer vision solutions.

Stay up-to-date on the latest advancements in computer vision and machine learning.

Qualifications:

Master's or Bachelor's degree in Computer Science or a related field.

5+ years of experience in computer vision and machine learning.

Strong programming skills in Python.

Experience with deep learning frameworks such as TensorFlow or PyTorch.

Familiarity with computer vision libraries such as OpenCV.

Excellent problem-solving and analytical skills.

Ability to work independently and as part of a team.

Additional Requirements:

Must be able to work from our office location at Pallavaram.

Must be able to start immediately.

Candidates with less than 5 years of experience will not be considered.

Key traits:

Should have excellent communication skills.

Should be self motivated and willing to work as part of a team.

Should be able to collaborate and coordinate in a remote environment.

Be a problem solver and be proactive to solve the challenges that come his way.

Technical Content Developer

About NxtWave

NxtWave was founded by Rahul Attuluri (ex-Amazon, IIIT Hyderabad), Sashank Reddy (IIT Bombay), and Anupam Pedarla (IIT Kharagpur). In February 2023, NxtWave raised 275 crore, led by Greater Pacific Capital, a leading international private equity firm. The startup is also backed by Orios Ventures, Better Capital, and marquee angels, including the founders of some of India's unicorns.

NxtWave is one of India's fastest-growing Ed-Tech startups, revolutionising the 21st-century job market by transforming youth into highly skilled tech professionals irrespective of their educational background with its CCBP 4.0 programs.

As an official partner for NSDC under the Ministry of Skill Development & Entrepreneurship, Government of India, and recognised by NASSCOM, the Ministry of Commerce and Industry, Government of India, and Startup India, NxtWave has earned a reputation for excellence. The startup has received accolades such as 'The Greatest Brand in Education' in a research-based listing by URS Media, a leading international media house. NxtWave has also been honoured with the "Startup Spotlight Award 2023" by T-Hub on its 8th Foundation Day.

Recently, NxtWave's founders, Anupam Pedarla and Sashank Gujjula, were honoured in the 2024 Forbes India 30 Under 30 for their exceptional contribution to transforming the tech education landscape in India.

The edtech brand eliminates the entry barrier to learning tech skills by offering vernacular content and interactive learning. Learning in one's mother tongue helps achieve higher comprehension, deeper attention, longer retention, and greater outcomes.

Presently, NxtWave has paid subscribers from 647+ districts across India. In just 2.5 years, CCBP 4.0 learners have been hired by 2000+ companies, including Google, Amazon, Nvidia, Goldman Sachs, Oracle, Deloitte, and more.

Know more about NxtWave:

• Our LinkedIn page: Link
• Next wave of opportunities with 1700+ companies: Link
• 33M funding news: Link
• YouTube Channel: Link
• Impact Stories on LinkedIn: Link

Read more about us in the news: Economic Times CNBC Yourstory VCCircle

Job Description:

At NxtWave, as a TCD, you will be heading your technical pod and will be responsible for creating and refining industry-relevant content for students. This role involves developing engaging content and materials that enhance learning experiences, ensuring industry relevance, and driving continuous improvement. You'll collaborate with teams internally and SMEs externally to manage technical content tracks, analyse industry trends, and deliver high-quality outcomes that improve learners' skills and employability.

Skills Required

• Minimum 1-4 years of experience in content development.
• Strong understanding of neural networks, CNNs, RNNs, LSTMs, GANs, transformers, and other advanced architectures.
• Expertise in Python and popular Deep Learning frameworks like TensorFlow, PyTorch, and Keras.
• Hands-on experience building, training, and deploying Deep Learning Models.
• Experience working with real-world datasets for image recognition, NLP, time series forecasting, and other domains.
• Knowledge of pre-trained models and transfer learning techniques.

Responsibilities

• Lead a team of Associate technical content developers to ensure timely and high-quality content creation.
• Develop high-quality, industry-relevant content on Deep Learning concepts, including neural networks, CNNs, RNNs, GANs, transformers, and other advanced topics.
• Incorporate hands-on projects and real-world datasets to provide practical learning experiences.
• Understanding of how to develop content to meet various learner needs and styles.
• Collaborate with Subject Matter Experts (SMEs) to ensure technical accuracy and relevance of the content.
• Design curriculum outlines, learning objectives, and roadmaps for comprehensive technical programs.
• Stay updated with the latest industry trends to incorporate cutting-edge topics and technologies into the content.
• Review and revise content regularly based on learner feedback and emerging educational strategies.
• Act as a bridge between content development and product teams, driving feature development and design improvements to support immersive and practical learning experiences.
• Monitor and report on the progress of content development to ensure alignment with organisational goals.

• Location: NxtWave Office Spaces in Hyderabad
• Job Type: Full-Time
• Working days: 5 days a week

Project Description:

• Our client, a leading manufacturer of high-end household appliances, is expanding into new smart product lines. As part of this strategic initiative, a large-scale global program is being implemented across the company's IP portfolio. This includes the development of new embedded software, enhancements to cloud infrastructure.

Responsibilities:

• - Work directly with computer vision perception engineers and camera ISP engineers.
• - Define the image quality requirements for customer use cases
• - Develop image processing algorithms to optimizing the camera image quality for performance improvement of computer vision applications
• - Develop custom ML models for visual recognition and training it from scratch
• - Quantization and pruning the ML models
• - Data evaluation using relevant metrics to increase the model performance
• - Implement computer vision/image processing algorithms in fast/optimal C++ code
• - Implement heuristic/math algorithms and probabilistic methods for camera AI
• - Research existing C++ solutions for CV logic
• - Develop cross-platform C++ code
• - Cover algorithms and CV code with unitest
• - Ramp up on new technologies related to CV/ML/algorithms (not R&D)
• - Integrate machine learning modules into algorithms

Mandatory Skills Description:

• - Bachelor's Degree or greater in Computer Science, Software Engineering, or related field.
• - 3-5 years of experience developing computer vision applications/algorithms
• - Participate in at least 2 image processing/computer vision projects, and 1 of them productized
• - Image processing / computer vision background/understanding
• - Strong C/C++ experience
• - Experience with OpenCV and ML frameworks such as Pytorch, TensorFlow, TensorFlow Lite
• - Deep understanding of neural network architectures
• - Experience to work with embedded devices in C++ on Linux
• - Decent understanding geometry 2D/3D and algebra.
• - Desire to learn new technologies
• - Desire to follow test driven development(in CV/ML too)

Nice-to-Have Skills Description:

• - Experience with machine learning integration, inference engines
• - Machine learning general understanding
• - Building for Android/Yocto experience
• - OpenCL/CUDA or OpenGL/Vulkan(shaders) experience/understanding and desire to learn or any different SIMD language

Languages:

• English: C1 Advanced

1. Programming & Software Engineering

- Strong proficiency in Python

- System design

- Git version control

2. Machine Learning & Deep Learning

- Production-level Machine Learning Engineering experience

- Machine Learning & Deep Learning fundamentals

- PyTorch and TensorFlow

3. Generative AI & LLM Engineering

- LLMs and Generative AI

- Retrieval-Augmented Generation (RAG)

- LangGraph

- MCP (Model Context Protocol)

4. Data Engineering & Feature Management

- Pandas, NumPy, SciPy for data processing

- Data versioning and dataset management

- Feature engineering and feature stores

5. MLOps & Model Lifecycle

- CI/CD for ML (pipelines, deployment strategies)

- Model versioning

- Model monitoring and observability (latency, throughput, logging, tracing)

- Data drift and concept drift detection

6. Infrastructure & Platform Engineering

- Docker and containerization

- Kubernetes

- On-premise infrastructure experience

- Hardware knowledge (CPU/GPU, memory, storage optimization)

7. Data Stores & Messaging Systems

- Redis

- MongoDB and PostgreSQL

- Message queues (RabbitMQ)

8. Preferred Skills (Any of the following)

- Model serving frameworks (Triton, SGLang, vLLM)

- Vector database (Qdrant)

- Experiment tracking tool (MLflow)

- CI/CD tools (GitLab CI)

- Networking (NGINX)

About Company:

Kinexcs is an AI-driven digital health platform and wearables company with a mission to enable and empower people for mobility and a better quality of life. It is focused on reducing the burden of musculoskeletal conditions, which affects about 25% of the world's population. Their 1st product, KIMIA Recover, a continuous monitoring device for the knee joint, has obtained CE marking and HSA approval and booked revenues from large implant companies and hospitals. It has recently won the Innomatch grant by the Temasek Foundation (one of the 6 among 256 companies across 32 countries), and is currently deployed in some of the largest public hospitals in Singapore. KIMIA Recover is a comprehensive recovery management solution that provides a reduction in the number of hospital visits and complications. The product was also the National Winner and International Top 20 of the prestigious James Dyson Award 2020 and has gained traction with major public hospitals and orthopaedic clinics. Their digital therapy platform, consisting of KIMIA Assess and KIMIA Move products, uses artificial intelligence technology for digital MSK assessment and therapy. This platform is capable of providing pre-consultation assessments and real-time exercise guidance and feedback to musculoskeletal patients and is being adopted by some of the largest private healthcare providers in the region, along with large corporate players.

Job Summary:

We are building a next-generation digital rehabilitation and fitness platform that combines computer vision, wearable sensors, and AI-driven analytics to improve patient recovery outcomes and long-term physical performance.

As a CV/ML Scientist / Engineer, you will play a core role in transforming our current computer vision pipeline into a robust, clinically reliable, and scalable system, and in developing proprietary sensor + vision fusion algorithms that become a long-term IP moat for the company.

This is not a "model fine-tuning only" role.

You will work on real-world data, noisy environments, diverse patient populations, and production constraints in healthcare.Key Responsibilities

Computer Vision & Pose Estimation

• Design, evaluate, and improve human pose estimation pipelines for rehab and fitness use cases
• Enhance robustness across:
• Clothing variations (loose clothing, hijabs, patterns)
• Lighting and camera placement
• Different body types, mobility limitations, and assistive devices
• Evaluate, integrate and sutomize models such as:
• MoveNet
• HRNet
• MediaPipe (customized / extended)
• Other open-source or commercial CV frameworks

Sensor Fusion & Motion Analysis

• Develop sensor + computer vision fusion algorithms combining:
• IMU / wearable sensor data
• RGB video-based pose estimation
• Design temporal models to improve:
• Angle accuracy (ROM)
• Motion smoothness
• Repetition counting
• Exercise quality validation
• Build algorithms resilient to partial occlusion or sensor dropout

Rehabilitation & Fitness Intelligence

Develop exercise validation and scoring algorithms:

Range of motion (ROM)

Speed, symmetry, control, compensation detection

• Contribute to personalized analytics using continuous ROM and exercise data
• Support wound image analytics and recovery progression insights (future roadmap)

Data & ML Engineering

Build pipelines for:

Data preprocessing and labeling

Model evaluation and benchmarking

Offline experimentation and online inference

Collaborate with backend engineers to deploy models in:

Real-time mobile pipelines

Cloud-based batch and streaming workflows

• Balance accuracy, latency, and compute cost

Clinical & Product Collaboration

• Work closely with:
• Product managers
• UX designers
• Clinicians and rehab specialists
• Translate clinical requirements into measurable technical metrics
• Support validation studies and algorithm documentation (DHF-ready)

Required Qualifications

Technical Skills

• Strong background in Computer Vision and Machine Learning
• Solid experience with:
• Python
• PyTorch or TensorFlow
• Hands-on experience with:
• Human pose estimation
• Time-series analysis
• Model optimization for real-time inference
• Understanding of signal processing fundamentals (IMU / motion data)

Practical Experience

• Experience deploying ML models into production, not just research
• Ability to debug models using real-world data, not curated datasets
• Comfortable working with imperfect, noisy, and limited datasets

Nice-to-Have (Strong Plus)

• Experience in healthcare, rehab, biomechanics, sports science, or fitness tech
• Sensor fusion experience (vision + IMU)
• Experience with:
• Temporal models (LSTM, TCN, transformers for motion)
• Edge or mobile ML optimization
• Familiarity with regulatory or compliance environments (HIPAA, GDPR, medical devices)
• Publications, patents, or open-source contributions in CV / ML

Hiring: Machine Learning Engineer (MLOps) - Coimbatore 3-5 Years

Location: Coimbatore

Experience: 3-5 Years

We are seeking a highly motivated and detail-oriented Machine Learning Engineer (MLOps) to join our organization. The selected candidate will be responsible for building, deploying, and maintaining scalable machine learning pipelines and ensuring the reliability of production ML systems.

Key Responsibilities:

• Design, develop, and manage end-to-end MLOps pipelines
• Deploy, monitor, and maintain machine learning models in production
• Implement CI/CD processes for efficient model lifecycle management
• Collaborate with data scientists and engineering teams to operationalize models
• Ensure system scalability, performance, and reliability

Required Qualifications:

• Minimum 2 years of hands-on experience in MLOps
• Strong programming skills in Python
• Experience with model deployment, monitoring, and automation frameworks

Preferred Qualifications:

• Exposure to frontend technologies such as Streamlit, TypeScript, Angular, or React
• Ability to develop dashboards or user interfaces for ML applications

Job Summary: We are seeking a talented and motivated Machine Learning Engineer to join our team. The ideal candidate will have a strong background in machine learning algorithms, data analysis, and software development. You will be responsible for designing, developing, and deploying machine learning models and systems that drive our products and services.

Key Responsibilities:

• Develop and implement machine learning algorithms and models.
• Design and conduct experiments to evaluate the performance of machine learning models.
• Collaborate with cross-functional teams to integrate machine learning solutions into products.
• Analyze large datasets to extract meaningful insights and patterns.
• Optimize and tune machine learning models for performance and scalability.
• Monitor and maintain deployed models, ensuring their performance in a production environment.
• Stay up-to-date with the latest trends and advancements in machine learning and artificial intelligence.
• Write clean, maintainable, and efficient code.
• Document processes, experiments, and results comprehensively.

Requirements:

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related field.
• Proven experience as a Machine Learning Engineer or similar role.
• Strong understanding of machine learning algorithms and techniques (e.g., supervised and unsupervised learning, reinforcement learning, deep learning).
• Proficiency in programming languages such as Python, R, or Java.
• Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
• Familiarity with data preprocessing and feature engineering techniques.
• Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) is a plus.
• Strong problem-solving skills and the ability to work independently and as part of a team.
• Excellent communication and collaboration skills.

Preferred Qualifications:

• Experience with natural language processing (NLP) and computer vision.
• Knowledge of big data technologies (e.g., Hadoop, Spark).
• Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes).
• Experience with version control systems (e.g., Git).
• Understanding of DevOps practices and CI/CD pipelines.

We are hiring Machine Learning (ML) Engineer who will be to focus on productionizing Machine Learning (ML) and large language model (LLM) features.

Looking for Minimum 5 + years of IT experience.

Location: Hyderabad, Telangana (Hybrid)

Duration: Full Time

Working Hours: Australia Time Zone (4.30 am IST - 1.30pm IST)

Availability: 15-30 days (Immediate Joiners)

Responsibilities:

• The responsibilities for this role are divided into approximately two-thirds AWS cloud/DevOps tasks and one-third ML/AI tasks.

• Design, implement, and maintain scalable AWS cloud infrastructure using services such as EC2, Lambda, DynamoDB and CloudFormation.
• Automate CI/CD pipelines for ML model training and deployment with tools like Azure DevOps.
• Monitor, troubleshoot, and optimise cloud environments for reliability, cost efficiency, and performance.
• Manage data and model versioning using tools such as MLflow or AWS SageMaker.
• Ensure compliance with data privacy, corporate, and regulatory standards across ML workflows.
• Integrate and deploy GenAI models in enterprise environments, addressing challenges related to scalability and security.
• Containerize ML applications using Docker and orchestrate deployments with AWS ECS.
• Experience with Bedrock, AgentCore, LiteLLM, LangFuse, LangChain, and LangGraph highly regarded as a bonus.

As a Senior Manager of Machine Learning Science at Wayfair, you'll shape the future of how millions of customers are engaged through intelligent, timely, and personalized communication powered by scalable AI systems.

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) science team builds and owns all the Machine Learning (ML) products that power search, marketing, and recommendation technologies across Wayfair. Our algorithms tackle a 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 and diverse assortment of home goods, to driving marketing experiences customers love at web scale.

Within this, the Notifications Science team focuses on building intelligent systems that optimize how and when we engage with customers across channels such as Email, Push, and SMS. These systems balance short-term engagement with long-term customer value, enabling personalized, relevant, and timely communication at scale.

About the Role

We are looking for an experienced machine learning leader to drive the research, development, and execution of ML systems that power Wayfair's customer engagement and notification platforms. This includes send decisioning, cross-channel optimization, and personalized content selection.

In this role, you will be responsible for defining the technical vision and roadmap for Wayfair's notifications ML stack and leading a team of scientists to deliver high-impact solutions at scale. You will oversee systems that determine which customers to contact, when to contact them, through which channel, and with what content. You will collaborate closely with engineering, analytics, and product partners within CT to align on priorities, execute against roadmaps, and scale impact. This role is a key leadership position within the CT Science organization, with visibility across marketing, product, and platform teams.

What You'll Do

• Lead and grow a team of machine learning scientists, providing mentorship, guidance, and career development
• Set the technical direction and long term roadmap for notification send decisioning, cross-channel optimization, and personalization systems
• Oversee the development of machine learning systems for notification decisioning under competing objectives, including ranking and personalization components
• Drive the design of multi-objective optimization frameworks balancing short-term engagement, long-term customer value, and customer fatigue
• Partner closely with product and engineering leaders to define strategy, prioritize initiatives, and deliver scalable ML solutions
• Establish best practices for model development, experimentation, and evaluation, ensuring high standards of quality and rigor
• Ensure successful end-to-end execution, from problem definition and modeling to experimentation and production deployment
• Influence and align with the broader ML leadership community at Wayfair on best practices, standards, and cross-team initiatives
• Maintain a strong customer-centric perspective in how problems are framed and solved

What You'll Need

• Bachelor's or Master's degree in Computer Science, Mathematics, Statistics or a related field
• 10+ years of experience building large-scale machine learning systems
• 1-2 years of experience managing or mentoring machine learning scientists
• Strong experience in owning ML-driven products or platforms, ideally in ranking, recommendations, ads, or notification systems
• Proven ability to define and execute on technical roadmaps in cross-functional environments
• Strong theoretical understanding and practical experience applying statistical models and ML algorithms at scale
• Proficiency in Python and ML frameworks such as TensorFlow or PyTorch
• Strong communication and stakeholder management skills, with the ability to influence across teams

Nice to Have

• PhD in Computer Science, Mathematics, Statistics or related field
• Experience with marketing technology, customer engagement platforms, or send optimization systems
• Familiarity with multi-objective optimization, causal inference, or experimentation frameworks
• Experience with Generative AI and its applications in personalization or content generation
• Experience with cloud platforms (GCP, AWS, Azure) and ML orchestration tools (Airflow, Kubeflow, MLflow) and agentic workflows

Job title: Machine learning engineer (Gen AI)

Location: Bangalore

experience: 5 To 7Years

Key responsibilities

• Advise clients on GenAI use cases, strategies, and provide feedback for product roadmap
• Evaluate and recommend suitable GenAI models and solutions for client's problem statement
• Design and prototype GenAI features and applications, focusing on user needs and value
• Keep up with the latest advancements in AI technologies, including Gen AI and the latest features of popular tools (RAG)
• Collaborate with engineering and design teams to ensure successful product integration
• Collect, clean, and preprocess data using appropriate tools and libraries, ensuring compatibility with AI algorithms and frameworks
• Train, test, and evaluate AI models employing appropriate evaluation metrics
• Optimize and fine-tune models for performance, scalability and efficiency
• Implement and deploy AI solutions in production environments under different frameworks
• Ability to build applications and use appropriate prompting on generative AI models
• Strong experience in Natural Language Processing, Large Language Models, and RAG approaches
• Assess the ethical implications and risks associated with GenAI product deployments
• Support pre-sales activities by providing GenAI expertise and contributing to product roadmaps

Skills and attributes for success - To qualify for the role you must have

• Bachelor's /Master's degree in computer science, data science, engineering, or a related field.
• Strong hands-on background in AI, machine learning, deep learning, and statistical modeling
• Proficiency in programming languages such as Python, Java
• Strong understanding of existing GenAI services
• Strong understanding of data structures, algorithms and AI project lifecycle

What we look for

• Strong analytical, problem solving and critical thinking skills
• Desire to investigate and try-out new tools and technologies as they are released
• Ability to work under tight timelines, in cases for multiple project deliveries
• Good interpersonal skills and ability to work effectively within high-performing teams
• Confidence to convey technical advice and guidance to clients
• Ability to adapt in a fast-paced multinational environment
• Evidence of self-motivation for continuous development

About Tekion:

Positively disrupting an industry that has not seen any innovation in over 50 years, Tekion has challenged the paradigm with the first and fastest cloud-native automotive platform that includes the revolutionary Automotive Retail Cloud (ARC) for retailers, Automotive Enterprise Cloud (AEC) for manufacturers and other large automotive enterprises and Automotive Partner Cloud (APC) for technology and industry partners. Tekion connects the entire spectrum of the automotive retail ecosystem through one seamless platform. The transformative platform uses cutting-edge technology, big data, machine learning, and AI to seamlessly bring together OEMs, retailers/dealers and consumers. With its highly configurable integration and greater customer engagement capabilities, Tekion is enabling the best automotive retail experiences ever. Tekion employs close to 3,000 people across North America, Asia and Europe.

Build and operate the production backbone that takes models from Applied Sciences (AS) and delivers reliable, low-latency ML services across Tekion's DMS, CRM, Digital Retail, Service, Payments, and enterprise products. You'll own pipelines, microservices, CI/CD, observability, and runtime reliability-working hand-in-hand with Applied Sciences and Product to turn ideas into measurable dealer and consumer impact.

Why this Role Matters

• Accelerate the rollout of LLM-powered and agent-driven features across Tekion products.
• Enable agentic workflows that automate, reason, and interact on behalf of users and internal stakeholders.
• Operationalize secure, compliant, and explainable LLM and agentic services at scale.
• Convert Applied Sciences models into scalable, compliant, cost efficient production services.
• Standardize how models are trained, validated, deployed, and monitored across Tekion products.
• Power real-time, context-aware experiences by integrating batch/stream features, graph context, and online inference.

What You'll Do

• Turn Applied Sciences prototype models (tabular, NLP/LLM, recommendation, forecasting) into fast, reliable services with well-defined API contracts.
• Integrate with the LLM Gateway/MCP, prompt/config versioning.
• Build and orchestrate CI/CD pipelines.
• Review data science models; refactor and optimize code; containerize; deploy; version; and monitor for quality.
• Collaborate with data scientists, data engineers, product managers, and architects to design enterprise systems.
• Monitor, detect, and mitigate risks unique to LLMs and agentic systems.
• Implement prompt management: versioning, A/B testing, guardrails, and dynamic orchestration based on feedback and metrics.
• Design batch/stream pipelines (Airflow/Kubeflow, Spark/Flink, Kafka) and online features linked to our domain graph.
• Build inference microservices (REST/gRPC) with schema versioning, structured outputs, and stringent p95 latency targets.
• Manage the model/feature lifecycle: feature store strategy, model/agent registry, versioning, and lineage.
• Instrument deep observability: traces/logs/metrics, data/feature drift, model performance, safety signals, and cost tracking.
• Ensure real-time reliability: autoscaling, caching, circuit breakers, retries/fallbacks, and graceful degradation.
• Develop templates/SDKs/CLIs, sandbox datasets, and documentation that make shipping ML the default path.

Desired Skills and Experience

• 6+ years in ML engineering/MLOps or backend/platform engineering with production ML.
• Experience with LLMs, retrieval systems, vector stores, and graph/knowledge stores.
• Strong software engineering fundamentals: Python plus one of Java/Go/Scala; API design; concurrency; testing.
• Hands-on with orchestration frameworks and libraries (LangChain, LlamaIndex, OpenAI Function Calling, AgentKit, etc.).
• Knowledge of agent architectures (reactive, planning, retrieval-augmented agents), and safe execution patterns.
• Pipelines and data: Airflow/Kubeflow or similar; Spark/Flink; Kafka/Kinesis; strong data quality practices.
• Microservices and runtime: Docker/Kubernetes, service meshes, REST/gRPC; performance and reliability engineering.
• Model ops: experiment tracking, registries (e.g., MLflow), feature stores, A/B and shadow testing, drift detection.
• Observability: OpenTelemetry/Prometheus/Grafana; debugging latency, tail behavior, and memory/CPU hotspots.
• Cloud: AWS preferred (IAM, ECS/EKS, S3, RDS/DynamoDB, Step Functions/Lambda), with cost optimization experience.
• Security/compliance: secrets management, RBAC/ABAC, PII handling, auditability.

Preferred Mindset

• Product-oriented: You measure success by dealer and consumer outcomes, not just technical metrics.
• Reliability- and safety-first: You move fast with guardrails, rollbacks, and clear SLOs.
• Systems thinker: You design for multi-tenant scale, portability, and cost efficiency
• Collaborative: You translate between Applied Sciences, Product, and the Data & AI Platform; you document and teach.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

Job Title: MLOps Engineer (6-8 Years Experience)

Location: Remote India

Project Type: Permanent Role

Working Hours: 8 AM - 5 PM UK Time

Engagement Work: Migration of Data Science models over next few months, followed by involvement in data science model creation, operations and enhancements

Role Overview

The client is currently operating data workflows in GCP and is in the process of migrating their only the Data Science workloads to Azure Databricks. Input data originates in GCP (was and will continue to) , data science workflows to be executed in Azure Databricks (end state), and model outputs are written back to GCP (was and will continue to).

The MLOps Engineer will play a critical role in supporting model migration, building and optimizing MLOps workflows, and eventually contributing to the broader data movement automation and self serve capabilities across cloud environments.

This role requires someone who deeply understands Databricks internals, PySpark, CI/CD orchestration, and ML model operationalization. Along with knowledge of GCP and Azure.

Key Responsibilities

1. Model Migration & Optimization

• Support migration of existing ML models from GCP to Azure Databricks. Understand existing model architecture and replicate/optimize it in Azure Databricks.
• Work closely with the Data Science team to operationalize migrated models and further optimize to reduce compute cost and increase test coverag.

2. MLOps & Workflow Orchestration

• Set up robust CI/CD pipelines using GitHub Actions for ML model deployments in Databricks.
• Implement and manage MLflow for tracking, versioning, and managing model lifecycle.
• Build efficient and scalable Data & ML pipelines using Databricks + PySpark.

3. Cloud & Data Movement Support

• Collaborate with the Data Engineering team regarding data movement between GCP Azure Databricks. In the future, take over parts of cross cloud data movement from the DE team and build self serve automation for data flows
• Build pipelines where outputs from Azure Databricks must be transferred back to GCP.

4. Architecture & Best Practices

• Provide architectural inputs and workflow optimization guidance during and after migration.
• Ensure scalable, cost efficient, and reliable model execution in Databricks.
• Improve testing, monitoring, and performance tuning for migrated and future ML models.

Required Experience

• 6-8 years of experience in Data Engineering, ML Engineering, or MLOps roles.

Must Have Skills

• Strong hands on expertise in Databricks and deep understanding of how it works under the hood.
• Proficiency in PySpark: writing scalable jobs, understanding execution plans, and optimization techniques.
• Experience building CI/CD pipelines using GitHub Actions.
• Experience with MLflow for tracking and operationalizing ML models.
• Knowledge of integrating workflows between GCP and Azure ecosystems.
• Strong debugging, optimization, and cost efficiency mindset.

Good to Have

• Experience with cross cloud data movement patterns.
• Familiarity with DS model structures and ability to collaborate closely with DS teams.
• Exposure to model monitoring and alerts in a distributed/cloud environment.

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

Location - Only Mumbai (Hybrid)

About the Company:

Collinson is a global, privately-owned company dedicated to enhancing the travel experience for millions worldwide. With over 30 years of expertise, we help top brands like American Express, British Airways, and Sephora engage and retain high-value customers. Our innovative solutions, including the world-renowned Priority Pass, set the standard for customer loyalty in the Financial Services, Travel, and Retail sectors. With 2,000 passionate employees across 20 locations, we offer the chance to be part of a dynamic, entrepreneurial team that’s shaping the future of customer experiences worldwide. Join us and help create smarter, more rewarding journeys.

About the Role:

As a Machine Learning (ML) Engineer at Collinson, you will develop cloud-based ML pipelines, collect data from various business units, and productionize analytics using a centralized platform. You’ll provide analytical expertise to deliver solutions that meet global client needs. Your role involves driving innovation, exploring new frameworks, and collaborating with the data platform team to integrate ML workflows. Additionally, you will support team growth, resolve issues, and ensure high-quality outputs through technical expertise, problem-solving, and effective communication with stakeholders.

Key Responsibilities:

• Develop and maintain machine learning pipelines for data-driven products

Location:  Bengaluru, Karnataka

About the Company

Sustainability Economics.ai is a global organization pioneering the convergence of clean energy and AI, enabling profitable energy transitions while powering end-to-end AI infrastructure. By integrating AI-driven cloud solutions with sustainable energy, we create scalable, intelligent ecosystems that drive efficiency, innovation, and long-term impact across industries. Guided by exceptional leaders with deep expertise across finance, policy, and technology, we are committed to building impactful, future-ready solutions.

Role Summary

We are seeking a highly skilled and analytical  Machine Learning Engineer  to design, develop, and deploy advanced machine learning and forecasting models for real-world applications.

The ideal candidate will have strong expertise in  machine learning algorithms, time series analysis, and probabilistic forecasting , along with working knowledge of  optimization techniques  to enhance decision-making systems.

This role focuses on building  scalable, production-grade ML systems  and collaborating closely with data scientists, engineers, and domain experts to deliver impactful solutions.

Key Responsibilities

• Design, develop, and deploy  machine learning models
  

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