Top Machine Learning Jobs in India

As a Machine Learning Researcher, you'll lead research in applying ML to trading problems, develop deep learning models, and collaborate with quant researchers.

No responsibilities are provided in the job description. The title "Cloud Machine Learning Engineer" implies work on developing, deploying, and maintaining machine learning models and infrastructure in cloud environments.

Design, build, deploy, and monitor LLM-based GenAI applications and agentic systems. Implement RAG pipelines, vector DB integrations, AI backends (FastAPI/Flask), chat UIs, and cloud MLOps ensuring performance, scalability, security, and Responsible AI compliance.

Design, build, deploy, and monitor LLM-based GenAI applications and RAG/document-intelligence solutions. Implement agentic/multi-agent systems, backend AI services, chat UIs and copilots, integrate vector databases, and productionize models on cloud platforms ensuring performance, security, scalability, and Responsible AI compliance.

Lead initiatives to enhance the ML platform, mentor junior engineers, build scalable ML models, and ensure code quality while driving innovation in a startup environment.

The Machine Learning Engineer will develop and innovate ML models and their infrastructure, collaborate, mentor, and take ownership of impactful projects.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

This role involves developing scalable AI/ML models, working with cross-functional teams, following agile processes, and maintaining deployed solutions.

Lead design and production of computer vision biometric systems (face detection, quality, attributes, recognition). Build/train models, implement fairness benchmarking, own end-to-end ML pipelines on AWS, optimize for low-latency inference, and mentor engineers.

The role involves developing scalable AI/ML models, collaborating with cross-functional teams, maintaining production solutions, and driving experimentation in AI/ML techniques.

Develop and deploy AI solutions for virtual tire development, perform large-scale data exploration, feature engineering, modeling, visualization, and productionize models with cross-functional teams to improve engineering workflows.

Lead and scale an AI engineering team to design, train, optimize, and deploy deep learning models for 2D/3D medical imaging. Oversee model architecture, MLOps pipelines, clinical integrations (PACS/RIS), and optimize models for cloud, edge, and clinical hardware while mentoring engineers and ensuring regulatory and data privacy considerations.

The AI/ML Engineer will develop generative AI models, deploy ML models, write scalable code, and work with various AI technologies and frameworks.

Responsible for designing, training, and deploying machine learning models, leading teams, conducting experiments, and documenting processes for AI solutions.

Lead and scale an AI engineering team to develop, train, optimize, and deploy deep learning models for 2D/3D medical imaging. Drive model architecture choices (CNNs, Transformers, U-Net variants), implement MLOps pipelines, ensure cloud/edge clinical deployment, and collaborate with clinicians to integrate solutions into PACS/RIS workflows while meeting data privacy and compliance needs.

Lead and grow Gracenote's AI/ML and Data Science teams, establish technical rigor and operating cadence, manage cross-functional stakeholder delivery, oversee ML/GenAI/NLP/computer vision research and production deployments, drive MLOps and QA, automate recurring analyses, and evolve predictive analytics and market-research methodologies for media measurement.

The Head of Machine Learning will manage a high-performing ML team, ensure the success of ML systems, and drive initiatives across business partnerships. The role emphasizes strong production engineering and theoretical ML expertise.

Research and develop ML/AI models for financial markets: fine-tune LLMs, build benchmarks, analyze large noisy time-series market data, perform feature engineering, validate models on GPU clusters, and translate research into deployable trading signals while collaborating with researchers, traders, and engineers.

The Associate Architect - Machine Learning role involves developing cloud ML solutions on AWS, fine-tuning LLMs, and collaborating with teams for optimal model implementation.

The Senior Machine Learning Engineer will lead the architecture and implementation of a generative AI-powered video creative platform, focusing on optimizing video ad creation while collaborating with multiple teams.

Lead architecture, development, and deployment of scalable ML and Generative AI solutions. Own end-to-end model lifecycle, MLOps pipelines, cloud-native deployments (AWS), vector DB ingestion, LLM/embedding selection, CI/CD, and mentor junior engineers while collaborating with cross-functional teams.

Design, build, and maintain a scalable Python-based BDD automation platform for desktop and web apps. Integrate LLMs (Azure OpenAI) and orchestration libraries (e.g., LangChain) to accelerate BDD step classification and test authoring. Develop data pipelines, ensure CI/CD integration (Jenkins), manage configuration consistency, mentor automation engineers, and improve deployment, observability, and reporting for enterprise-grade QA automation.

As a Machine Learning Engineer, you will build and maintain ML infrastructure, develop credit models and collaborate on data-driven features. The role requires extensive hands-on experience in ML engineering within production environments.

Design, build, and productionize scalable ML and generative-AI capabilities for identity resolution, audience modeling, attribution, and optimization. Develop data pipelines, features, experiments, and monitoring; integrate LLM/Gen-AI features and partner with product, data science, and platform teams to drive advertiser outcomes.

Lead design and implementation of scalable AI/ML architectures and autonomous agent systems for debugging and power/performance optimization. Build analytics and ML models using telemetry to detect anomalies, predict performance, and deliver system-level optimizations while collaborating with hardware, software, and platform teams to deploy solutions.