Find Your Dream Machine Learning Job in India

Software Engineer - MLOps

Location: Bangalore, India

Experience Level: 2+ years

Type: Full-Time

Work Mode: In-office

About Our Client

Our client is a fast-growing startup revolutionizing inventory scanning in the warehouse industry. The team is agile, curious, and deeply passionate about building reliable and scalable systems across the stack-from MLOps pipelines to robust configuration infrastructure and distributed data pipelines.

Role Overview

We are looking for a Python Engineer who thrives in dynamic environments and has a strong grasp of software architecture and distributed systems. You will design and build core backend components that power an AI-driven warehouse intelligence platform. This is a high-impact role with opportunities to solve complex systems problems, work closely with cross-functional teams, and drive engineering best practices.

What You'll Do

• Design, implement, and maintain Python-based tools and services across MLOps, data pipelines, and configuration management.
• Architect scalable and modular components for high-throughput data processing using technologies like Kafka.
• Collaborate with ML engineers, platform developers, and DevOps to build efficient and maintainable infrastructure.
• Contribute to engineering discussions on design patterns, performance, and scalability.
• Write clean, modular, and well-documented code following solid software engineering principles.
• Take ownership of features end-to-end-from ideation and design to deployment and monitoring.

What We're Looking For

• 2+ years of experience developing and maintaining Python applications in production environments.
• Hands-on experience with NVIDIA Triton Inference Server, including model deployment, performance optimization, and integration into production workflows.
• Strong software design skills with a focus on modularity, scalability, and fault-tolerance.
• Experience with Kafka or similar distributed messaging systems.
• Solid understanding of version control (Git), CI/CD workflows, and containerization tools like Docker.
• Exposure to MLOps frameworks (e.g., MLflow, DVC, Airflow).
• Experience with FastAPI or similar Python-based web frameworks.
• Experience with infrastructure-as-code and structured config formats (e.g., YAML, JSON, Ansible).

You're a Great Fit If You Are

• Design-oriented - You care about abstraction, maintainability, and writing systems that are easy to reason about.
• Adaptable - You can switch contexts easily and learn new technologies with confidence.
• Collaborative - You enjoy working with cross-functional teams to deliver end-to-end features.
• Detail-conscious - You think deeply about edge cases, system behavior, and monitoring.

Software Engineer - MLOps

Location: Bangalore, India

Experience Level: 2+ years

Type: Full-Time

Work Mode: In-office

About Our Client

Our client is a fast-growing startup revolutionizing inventory scanning in the warehouse industry. The team is agile, curious, and deeply passionate about building reliable and scalable systems across the stack-from MLOps pipelines to robust configuration infrastructure and distributed data pipelines.

Role Overview

We are looking for a Python Engineer who thrives in dynamic environments and has a strong grasp of software architecture and distributed systems. You will design and build core backend components that power an AI-driven warehouse intelligence platform. This is a high-impact role with opportunities to solve complex systems problems, work closely with cross-functional teams, and drive engineering best practices.

What You'll Do

• Design, implement, and maintain Python-based tools and services across MLOps, data pipelines, and configuration management.
• Architect scalable and modular components for high-throughput data processing using technologies like Kafka.
• Collaborate with ML engineers, platform developers, and DevOps to build efficient and maintainable infrastructure.
• Contribute to engineering discussions on design patterns, performance, and scalability.
• Write clean, modular, and well-documented code following solid software engineering principles.
• Take ownership of features end-to-end-from ideation and design to deployment and monitoring.

What We're Looking For

• 2+ years of experience developing and maintaining Python applications in production environments.
• Hands-on experience with NVIDIA Triton Inference Server, including model deployment, performance optimization, and integration into production workflows.
• Strong software design skills with a focus on modularity, scalability, and fault-tolerance.
• Experience with Kafka or similar distributed messaging systems.
• Solid understanding of version control (Git), CI/CD workflows, and containerization tools like Docker.
• Exposure to MLOps frameworks (e.g., MLflow, DVC, Airflow).
• Experience with FastAPI or similar Python-based web frameworks.
• Experience with infrastructure-as-code and structured config formats (e.g., YAML, JSON, Ansible).

You're a Great Fit If You Are

• Design-oriented - You care about abstraction, maintainability, and writing systems that are easy to reason about.
• Adaptable - You can switch contexts easily and learn new technologies with confidence.
• Collaborative - You enjoy working with cross-functional teams to deliver end-to-end features.
• Detail-conscious - You think deeply about edge cases, system behavior, and monitoring.

Job Title: AI Engineer

Experience: 5-10Years

Location: Kolkata, Pune, PAN INDIA

Key Skills:

• Python & AI/ML engineering
• GenAI frameworks: Lang Chain, LangGraph, RAG
• Cloud platforms (AWS/Azure)
• LLMs, prompt engineering, Agentic AI
• MLOps tools (MLflow, Docker, Kubernetes)
• GitHub, basic DevOps (good to have)

Responsibilities:

• Build and deploy AI/GenAI solutions (RAG, LLM apps, pipelines)
• Design scalable ML/DL models and workflows
• Implement AI pipelines using MLOps best practices
• Collaborate with cross-functional teams and mentor juniors
• Ensure code quality, optimization, and deployment

Requirements:

• 3+ years in AI/ML engineering
• Strong experience in Python and LLM-based applications
• Hands-on with cloud deployment and AI frameworks
• Knowledge of Agentic AI and modern AI tools

Other:

• Domain: Energy & Resources (preferred)
• Strong communication, collaboration, and problem-solving skills
• Degree in CS/AI or related

AI / Machine Learning Engineer (Remote)

Note: To be considered, Please apply with a 2-minute max Loom Video on the following page.

"Along with your application, I want to hear about the most exceptional product you've built. Include a Loom video walking me through the product, the code and explain the most significant challenge you faced when working on it."

Salary - $40,000USD

We're hiring an AI / Machine Learning Engineer to join a fast-growing financial data & technology company building AI-powered products used by banks, fintechs, and trading platforms globally.

This role sits at the intersection of machine learning, backend engineering, and real-time data systems, with a strong focus on production-grade AI.

What you'll be working on

You'll be building systems that turn real-time financial data into actionable insights, leveraging modern ML and LLM capabilities.

Key Responsibilities

AI / Machine Learning

• Research, design, and deploy ML models across NLP, time-series forecasting, and event detection
• Build LLM-driven systems (summarization, RAG pipelines, embedding search)
• Develop model serving APIs and scalable inference layers (Python / Go)
• Implement monitoring, drift detection, and retraining pipelines
• Work with financial text (earnings calls, filings, news) to extract structured insights
• Partner with data teams on training datasets, feature stores, and embeddings

Backend & Infrastructure

• Build high-performance Python / Go microservices supporting AI systems
• Design and optimize real-time inference pipelines on AWS (EKS, ECS, S3, Lambda)
• Ensure low-latency, scalable, fault-tolerant systems
• Implement CI/CD for ML workflows (containerization, deployment, versioning)
• Collaborate with DevOps on infrastructure + observability

Required Experience

• 4+ years in AI/ML or data engineering, with production deployments
• Degree in Computer Science (or similar)
• Strong in Python (ML/data) and Go (backend/microservices)
• Experience with PyTorch, TensorFlow, or Hugging Face
• Understanding of transformers, embeddings, or LLM fine-tuning
• Strong grasp of data pipelines + ML lifecycle
• Experience with AWS (EKS, S3, Lambda, EC2)
• Comfortable working in distributed / remote teams

Nice to Have

• Startup experience
• Fintech / financial data exposure
• Experience building LLM APIs or RAG systems
• Knowledge of vector databases (Pinecone, Weaviate, FAISS, OpenSearch kNN)
• Experience with Kafka / streaming systems
• Exposure to MLOps tools (MLflow, Airflow, SageMaker, etc.)
• Open-source contributions (ML or Go)

️ Tech Stack

• Languages: Python, Go
• ML: PyTorch, TensorFlow, Hugging Face, LangChain
• Cloud: AWS (EKS, ECS, S3, Lambda, EC2, IAM)
• Infra: Docker, Kubernetes
• Data: Kafka, Postgres, OpenSearch
• CI/CD: GitHub Actions, GitLab CI
• Monitoring: Datadog, Prometheus, Grafana

Details

• Remote (EU / US overlap preferred)
• Full-time contractor (40+ hours/week)
• Competitive, flexible compensation
• Target start: ASAP

AI / Machine Learning Engineer (Remote)

Note: To be considered, Please apply with a 2-minute max Loom Video on the following page.

"Along with your application, I want to hear about the most exceptional product you've built. Include a Loom video walking me through the product, the code and explain the most significant challenge you faced when working on it."

Salary - $40,000USD

We're hiring an AI / Machine Learning Engineer to join a fast-growing financial data & technology company building AI-powered products used by banks, fintechs, and trading platforms globally.

This role sits at the intersection of machine learning, backend engineering, and real-time data systems, with a strong focus on production-grade AI.

What you'll be working on

You'll be building systems that turn real-time financial data into actionable insights, leveraging modern ML and LLM capabilities.

Key Responsibilities

AI / Machine Learning

• Research, design, and deploy ML models across NLP, time-series forecasting, and event detection
• Build LLM-driven systems (summarization, RAG pipelines, embedding search)
• Develop model serving APIs and scalable inference layers (Python / Go)
• Implement monitoring, drift detection, and retraining pipelines
• Work with financial text (earnings calls, filings, news) to extract structured insights
• Partner with data teams on training datasets, feature stores, and embeddings

Backend & Infrastructure

• Build high-performance Python / Go microservices supporting AI systems
• Design and optimize real-time inference pipelines on AWS (EKS, ECS, S3, Lambda)
• Ensure low-latency, scalable, fault-tolerant systems
• Implement CI/CD for ML workflows (containerization, deployment, versioning)
• Collaborate with DevOps on infrastructure + observability

Required Experience

• 4+ years in AI/ML or data engineering, with production deployments
• Degree in Computer Science (or similar)
• Strong in Python (ML/data) and Go (backend/microservices)
• Experience with PyTorch, TensorFlow, or Hugging Face
• Understanding of transformers, embeddings, or LLM fine-tuning
• Strong grasp of data pipelines + ML lifecycle
• Experience with AWS (EKS, S3, Lambda, EC2)
• Comfortable working in distributed / remote teams

Nice to Have

• Startup experience
• Fintech / financial data exposure
• Experience building LLM APIs or RAG systems
• Knowledge of vector databases (Pinecone, Weaviate, FAISS, OpenSearch kNN)
• Experience with Kafka / streaming systems
• Exposure to MLOps tools (MLflow, Airflow, SageMaker, etc.)
• Open-source contributions (ML or Go)

️ Tech Stack

• Languages: Python, Go
• ML: PyTorch, TensorFlow, Hugging Face, LangChain
• Cloud: AWS (EKS, ECS, S3, Lambda, EC2, IAM)
• Infra: Docker, Kubernetes
• Data: Kafka, Postgres, OpenSearch
• CI/CD: GitHub Actions, GitLab CI
• Monitoring: Datadog, Prometheus, Grafana

Details

• Remote (EU / US overlap preferred)
• Full-time contractor (40+ hours/week)
• Competitive, flexible compensation
• Target start: ASAP

AI / Machine Learning Engineer (Remote)

Note: To be considered, Please apply with a 2-minute max Loom Video on the following page.

"Along with your application, I want to hear about the most exceptional product you've built. Include a Loom video walking me through the product, the code and explain the most significant challenge you faced when working on it."

Salary - $40,000USD

We're hiring an AI / Machine Learning Engineer to join a fast-growing financial data & technology company building AI-powered products used by banks, fintechs, and trading platforms globally.

This role sits at the intersection of machine learning, backend engineering, and real-time data systems, with a strong focus on production-grade AI.

What you'll be working on

You'll be building systems that turn real-time financial data into actionable insights, leveraging modern ML and LLM capabilities.

Key Responsibilities

AI / Machine Learning

• Research, design, and deploy ML models across NLP, time-series forecasting, and event detection
• Build LLM-driven systems (summarization, RAG pipelines, embedding search)
• Develop model serving APIs and scalable inference layers (Python / Go)
• Implement monitoring, drift detection, and retraining pipelines
• Work with financial text (earnings calls, filings, news) to extract structured insights
• Partner with data teams on training datasets, feature stores, and embeddings

Backend & Infrastructure

• Build high-performance Python / Go microservices supporting AI systems
• Design and optimize real-time inference pipelines on AWS (EKS, ECS, S3, Lambda)
• Ensure low-latency, scalable, fault-tolerant systems
• Implement CI/CD for ML workflows (containerization, deployment, versioning)
• Collaborate with DevOps on infrastructure + observability

Required Experience

• 4+ years in AI/ML or data engineering, with production deployments
• Degree in Computer Science (or similar)
• Strong in Python (ML/data) and Go (backend/microservices)
• Experience with PyTorch, TensorFlow, or Hugging Face
• Understanding of transformers, embeddings, or LLM fine-tuning
• Strong grasp of data pipelines + ML lifecycle
• Experience with AWS (EKS, S3, Lambda, EC2)
• Comfortable working in distributed / remote teams

Nice to Have

• Startup experience
• Fintech / financial data exposure
• Experience building LLM APIs or RAG systems
• Knowledge of vector databases (Pinecone, Weaviate, FAISS, OpenSearch kNN)
• Experience with Kafka / streaming systems
• Exposure to MLOps tools (MLflow, Airflow, SageMaker, etc.)
• Open-source contributions (ML or Go)

️ Tech Stack

• Languages: Python, Go
• ML: PyTorch, TensorFlow, Hugging Face, LangChain
• Cloud: AWS (EKS, ECS, S3, Lambda, EC2, IAM)
• Infra: Docker, Kubernetes
• Data: Kafka, Postgres, OpenSearch
• CI/CD: GitHub Actions, GitLab CI
• Monitoring: Datadog, Prometheus, Grafana

Details

• Remote (EU / US overlap preferred)
• Full-time contractor (40+ hours/week)
• Competitive, flexible compensation
• Target start: ASAP

AI / Machine Learning Engineer (Remote)

Note: To be considered, Please apply with a 2-minute max Loom Video on the following page.

"Along with your application, I want to hear about the most exceptional product you've built. Include a Loom video walking me through the product, the code and explain the most significant challenge you faced when working on it."

Salary - $40,000USD

We're hiring an AI / Machine Learning Engineer to join a fast-growing financial data & technology company building AI-powered products used by banks, fintechs, and trading platforms globally.

This role sits at the intersection of machine learning, backend engineering, and real-time data systems, with a strong focus on production-grade AI.

What you'll be working on

You'll be building systems that turn real-time financial data into actionable insights, leveraging modern ML and LLM capabilities.

Key Responsibilities

AI / Machine Learning

• Research, design, and deploy ML models across NLP, time-series forecasting, and event detection
• Build LLM-driven systems (summarization, RAG pipelines, embedding search)
• Develop model serving APIs and scalable inference layers (Python / Go)
• Implement monitoring, drift detection, and retraining pipelines
• Work with financial text (earnings calls, filings, news) to extract structured insights
• Partner with data teams on training datasets, feature stores, and embeddings

Backend & Infrastructure

• Build high-performance Python / Go microservices supporting AI systems
• Design and optimize real-time inference pipelines on AWS (EKS, ECS, S3, Lambda)
• Ensure low-latency, scalable, fault-tolerant systems
• Implement CI/CD for ML workflows (containerization, deployment, versioning)
• Collaborate with DevOps on infrastructure + observability

Required Experience

• 4+ years in AI/ML or data engineering, with production deployments
• Degree in Computer Science (or similar)
• Strong in Python (ML/data) and Go (backend/microservices)
• Experience with PyTorch, TensorFlow, or Hugging Face
• Understanding of transformers, embeddings, or LLM fine-tuning
• Strong grasp of data pipelines + ML lifecycle
• Experience with AWS (EKS, S3, Lambda, EC2)
• Comfortable working in distributed / remote teams

Nice to Have

• Startup experience
• Fintech / financial data exposure
• Experience building LLM APIs or RAG systems
• Knowledge of vector databases (Pinecone, Weaviate, FAISS, OpenSearch kNN)
• Experience with Kafka / streaming systems
• Exposure to MLOps tools (MLflow, Airflow, SageMaker, etc.)
• Open-source contributions (ML or Go)

️ Tech Stack

• Languages: Python, Go
• ML: PyTorch, TensorFlow, Hugging Face, LangChain
• Cloud: AWS (EKS, ECS, S3, Lambda, EC2, IAM)
• Infra: Docker, Kubernetes
• Data: Kafka, Postgres, OpenSearch
• CI/CD: GitHub Actions, GitLab CI
• Monitoring: Datadog, Prometheus, Grafana

Details

• Remote (EU / US overlap preferred)
• Full-time contractor (40+ hours/week)
• Competitive, flexible compensation
• Target start: ASAP

AI / Machine Learning Engineer (Remote)

Note: To be considered, Please apply with a 2-minute max Loom Video on the following page.

"Along with your application, I want to hear about the most exceptional product you've built. Include a Loom video walking me through the product, the code and explain the most significant challenge you faced when working on it."

Salary - $40,000USD

We're hiring an AI / Machine Learning Engineer to join a fast-growing financial data & technology company building AI-powered products used by banks, fintechs, and trading platforms globally.

This role sits at the intersection of machine learning, backend engineering, and real-time data systems, with a strong focus on production-grade AI.

What you'll be working on

You'll be building systems that turn real-time financial data into actionable insights, leveraging modern ML and LLM capabilities.

Key Responsibilities

AI / Machine Learning

• Research, design, and deploy ML models across NLP, time-series forecasting, and event detection
• Build LLM-driven systems (summarization, RAG pipelines, embedding search)
• Develop model serving APIs and scalable inference layers (Python / Go)
• Implement monitoring, drift detection, and retraining pipelines
• Work with financial text (earnings calls, filings, news) to extract structured insights
• Partner with data teams on training datasets, feature stores, and embeddings

Backend & Infrastructure

• Build high-performance Python / Go microservices supporting AI systems
• Design and optimize real-time inference pipelines on AWS (EKS, ECS, S3, Lambda)
• Ensure low-latency, scalable, fault-tolerant systems
• Implement CI/CD for ML workflows (containerization, deployment, versioning)
• Collaborate with DevOps on infrastructure + observability

Required Experience

• 4+ years in AI/ML or data engineering, with production deployments
• Degree in Computer Science (or similar)
• Strong in Python (ML/data) and Go (backend/microservices)
• Experience with PyTorch, TensorFlow, or Hugging Face
• Understanding of transformers, embeddings, or LLM fine-tuning
• Strong grasp of data pipelines + ML lifecycle
• Experience with AWS (EKS, S3, Lambda, EC2)
• Comfortable working in distributed / remote teams

Nice to Have

• Startup experience
• Fintech / financial data exposure
• Experience building LLM APIs or RAG systems
• Knowledge of vector databases (Pinecone, Weaviate, FAISS, OpenSearch kNN)
• Experience with Kafka / streaming systems
• Exposure to MLOps tools (MLflow, Airflow, SageMaker, etc.)
• Open-source contributions (ML or Go)

️ Tech Stack

• Languages: Python, Go
• ML: PyTorch, TensorFlow, Hugging Face, LangChain
• Cloud: AWS (EKS, ECS, S3, Lambda, EC2, IAM)
• Infra: Docker, Kubernetes
• Data: Kafka, Postgres, OpenSearch
• CI/CD: GitHub Actions, GitLab CI
• Monitoring: Datadog, Prometheus, Grafana

Details

• Remote (EU / US overlap preferred)
• Full-time contractor (40+ hours/week)
• Competitive, flexible compensation
• Target start: ASAP

AI / Machine Learning Engineer (Remote)

Note: To be considered, Please apply with a 2-minute max Loom Video on the following page.

"Along with your application, I want to hear about the most exceptional product you've built. Include a Loom video walking me through the product, the code and explain the most significant challenge you faced when working on it."

Salary - $40,000USD

We're hiring an AI / Machine Learning Engineer to join a fast-growing financial data & technology company building AI-powered products used by banks, fintechs, and trading platforms globally.

This role sits at the intersection of machine learning, backend engineering, and real-time data systems, with a strong focus on production-grade AI.

What you'll be working on

You'll be building systems that turn real-time financial data into actionable insights, leveraging modern ML and LLM capabilities.

Key Responsibilities

AI / Machine Learning

• Research, design, and deploy ML models across NLP, time-series forecasting, and event detection
• Build LLM-driven systems (summarization, RAG pipelines, embedding search)
• Develop model serving APIs and scalable inference layers (Python / Go)
• Implement monitoring, drift detection, and retraining pipelines
• Work with financial text (earnings calls, filings, news) to extract structured insights
• Partner with data teams on training datasets, feature stores, and embeddings

Backend & Infrastructure

• Build high-performance Python / Go microservices supporting AI systems
• Design and optimize real-time inference pipelines on AWS (EKS, ECS, S3, Lambda)
• Ensure low-latency, scalable, fault-tolerant systems
• Implement CI/CD for ML workflows (containerization, deployment, versioning)
• Collaborate with DevOps on infrastructure + observability

Required Experience

• 4+ years in AI/ML or data engineering, with production deployments
• Degree in Computer Science (or similar)
• Strong in Python (ML/data) and Go (backend/microservices)
• Experience with PyTorch, TensorFlow, or Hugging Face
• Understanding of transformers, embeddings, or LLM fine-tuning
• Strong grasp of data pipelines + ML lifecycle
• Experience with AWS (EKS, S3, Lambda, EC2)
• Comfortable working in distributed / remote teams

Nice to Have

• Startup experience
• Fintech / financial data exposure
• Experience building LLM APIs or RAG systems
• Knowledge of vector databases (Pinecone, Weaviate, FAISS, OpenSearch kNN)
• Experience with Kafka / streaming systems
• Exposure to MLOps tools (MLflow, Airflow, SageMaker, etc.)
• Open-source contributions (ML or Go)

️ Tech Stack

• Languages: Python, Go
• ML: PyTorch, TensorFlow, Hugging Face, LangChain
• Cloud: AWS (EKS, ECS, S3, Lambda, EC2, IAM)
• Infra: Docker, Kubernetes
• Data: Kafka, Postgres, OpenSearch
• CI/CD: GitHub Actions, GitLab CI
• Monitoring: Datadog, Prometheus, Grafana

Details

• Remote (EU / US overlap preferred)
• Full-time contractor (40+ hours/week)
• Competitive, flexible compensation
• Target start: ASAP

About mlHealth 360

mlHealth 360 is a Health-Tech company dedicated to revolutionizing healthcare

through deep learning-powered medical image screening. We empower

healthcare professionals and institutions with innovative AI tools to improve

patient outcomes, reduce costs, and enhance efficiency. Our focus is on

developing state-of-the-art models (CNNs, Transformers, LLMs, and VLMs) for

automated diagnosis, segmentation, and clinical decision support.

Role Description

As an ML Engineer at mlHealth 360, you will be a hands-on technical leader,

driving the end-to-end development of AI-powered diagnostic tools. This role

requires expertise in research-grade deep learning and production-grade

MLOps to deploy scalable, high-performance models in clinical settings. You will

bridge R&D experimentation and engineering execution, ensuring models are

clinically robust, efficient, and integrated into real-world workflows.

Key Responsibilities

1. Deep Learning Model Development

• Design, implement, and train advanced architectures (U-Net, CNN, ViT, 3D CNNs/Transformers, VLM, LLM) for segmentation, Report Generation, detection, classification, and multi-modal analysis in medical imaging.

• Implement and adapt state-of-the-art algorithms and model architectures based on cutting-edge research papers.

• Apply self-supervised learning (e.g., contrastive learning), reinforcement

learning (RL), and diffusion models for robust and adaptive solutions.

• Optimize models for high accuracy, low latency, and clinical interpretability using techniques like LoRA, adapter tuning, attention mechanisms, and multi-modal fusion (imaging + clinical text/EHRs).

• Develop solutions for 3D volumetric data (CT, MRI) and real-world clinical deployment.

2. End-to-End Data Pipelines

• Build and maintain scalable pipelines for ingesting, preprocessing, and versioning DICOM/NIfTI datasets (CT, MRI, X-ray).

• Automate data augmentation, normalization, and annotation using tools like Encord, MONAI, and OpenCV.

• Ensure data privacy and compliance with HIPAA/GDPR standards.

3. MLOps & Deployment

• Containerize models using Docker and deploy them on Kubernetes for scalable, low-latency inference in clinical environments.

• Implement MLOps best practices: automated testing, model monitoring, continuous training (CT) loops, and A/B testing.

• Optimize models for edge deployment (quantization, pruning, ONNX/TensorRT acceleration).

4. Research & Innovation

• Stay updated with SOTA research in medical imaging (e.g., foundation models, diffusion models) and prototype novel solutions.

• Publish findings in conferences/journals and contribute to open-source projects.

Required Experience

• 2+ years of hands-on experience in building and deploying ML models, with a focus on medical imaging or computer vision.

• 1+ year of experience in medical image analysis (CT, MRI, X-ray) and radiological workflows.

• Proficiency in:

o Deep Learning Frameworks: PyTorch, TensorFlow.

o Medical Imaging Libraries: MONAI, ITK, SimpleITK, pydicom.

o Annotation Tools: Encord, Labelbox, CVAT.

o MLOps & Cloud Platforms: AWS, Azure, Databricks, Docker, MLflow, Kubernetes.

o Production Deployment: Model optimization (quantization, ONNX, TensorRT), edge deployment, and scalable inference.

Education

• Bachelor's, Master's in Computer Science, Biomedical Engineering, Data Science, or a related field, with a portfolio of deployed ML projects in healthcare (e.g., GitHub, research publications, or product demos).

Why Join mlHealth 360?

• Work on impactful AI solutions that directly improve patient care.

• Collaborate with cross-functional teams (engineers, clinicians, researchers).

• Access to cutting-edge tools, datasets, and research opportunities.

• Growth opportunities: Mentorship, conferences, and access to cutting-edge research in medical AI.

6+ years of experience in AI and machine learning, out of which 3+ years of experience in machine learning engineering, NLP, Generative AI, and LLM technologies.

Experience with LLM Agentic workflows and frameworks (Langchain, LangGraph, LlamaIndex, etc.).

Knowledge and experience with transformer architectures, prompt engineering, retrieval-augmented generation (RAG), guardrails, and LLM evaluation methodologies.

Hands-on experience of MLOps and LLMOps is desired.

Knowledge of design, implementation and management of distributed training pipelines for LLMs to ensure scalability and efficiency are nice to have.

Advanced knowledge of Python and some experience in ML frameworks (PyTorch, scikit-learn, etc.) for training, fine-tuning, and deploying generative models is desired.

Experience with Cloud technology, preferably AWS.

Experience with Voice Conversational AI will be a big plus.

Ability to contribute both independently and as part of a team.

Excellent analytical and problem-solving skills.

Excellent listening, communication, interpersonal and presentation skills to articulate complex technical ideas to cross-disciplinary internal and external stakeholders

About the Role

We are looking for a Senior Machine Learning Engineer to design, build, and scale production-grade ML and GenAI systems .

In this role, you will own the end-to-end lifecycle of ML solutions — from problem formulation and model development to deployment, monitoring, and continuous improvement . You will play a key role in building LLM-powered applications and scalable ML systems that power critical business use cases, including ESG analytics.

This role requires a strong balance of machine learning expertise, software engineering practices, and real-world deployment experience .

Responsibilities

Machine Learning & Modeling

• Design and develop ML models for structured and unstructured data (classification, NLP, time series).
• Perform feature engineering, model selection, and hyperparameter tuning.
• Evaluate models using appropriate metrics (precision, recall, F1, ROC-AUC, latency, cost).

GenAI & LLM Systems

• Build and optimize LLM-based applications using techniques such as:
• Retrieval-Augmented Generation (RAG)
• Prompt engineering and prompt optimization
• Context management and response evaluation
• Understand and mitigate challenges such as hallu

** Immediate Joiners Only - Please apply only if you're available to start right away. If you're currently serving a notice period or cannot join immediately, we kindly ask that you refrain from applying.​ **

*** We're only interested in candidates who have experience working with AI Agent frameworks —such as LangChain, Langraph , AutoGen, CrewAI, Auto-GPT, or custom-built systems. If you've explored multi-agent coordination, autonomous task execution, memory management, or tool-based reasoning using LLMs,***

Position: Machine Learning Engineer (Entry-Level, LLM Focus)

Location: Fully Remote (India-Based)

Start Date: Immediate

Format: Full time

Compensation: Competitive Salary + Strong Equity Offering + Benefits

Who We Are

Gabeo.ai is a cutting-edge health tech startup based in the Bay Area (USA), collaborating with industry leaders like Astrana Health, Athena Health and Rebound Orthopedics. Our goal? Transform the healthcare revenue cycle by using advanced AI to tackle denied medical claims—one of the sector’s biggest financial headaches.

At Gabeo.ai, we pride ourselves on:

• Innovation: We push boundaries and welcome out-of-the-box thinking.
• Collaboration: We’re a tight-knit team that values open communication.
• Impact: Your work

Job title: Machine learning engineer (Gen AI)

Location: Bangalore

experience: 4 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


EdgeVerve



5 days ago