Top Machine Learning Jobs in India

Create and review realistic, locale-specific robotics and natural sciences tasks in Tamil to evaluate AI systems; apply scoring rubrics, review and score submissions for accuracy and regulatory alignment, and produce gold-standard solutions and expert feedback.

Lead APAC revenue operations and strategy for InMobi's ads portfolio. Build regional GTM strategy, performance frameworks, and operating cadence (WBR/MBR/QBR). Drive cross-functional alignment with Sales, Product, Marketing, and Partnerships, and lead initiatives on market expansion, pricing, segmentation, and monetization.

Lead product definition, research product-market fit, create process flows, UI mockups and documentation, coordinate with engineering on APIs and ML integrations, manage SDLC, prepare release notes and marketing materials, conduct demos and training, and support pricing/RFPs.

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

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 an AI/ML Engineer, you will develop machine learning models, collaborate with teams, preprocess data, and optimize ML pipelines. Additionally, you will mentor junior members and contribute to AI/ML strategies.

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.

The role involves designing and implementing production ML systems, mentoring junior engineers, and establishing best practices for ML performance and evaluation.

Develop and optimize machine learning models, manage project priorities, and improve real-world solutions using advanced ML techniques.

Research, prototype, and implement generative and ML algorithms; design, develop, deploy, and maintain Models-as-a-Service; lead MLOps best practices and mentor engineers; collaborate across Data, Cloud, and Architecture teams to deliver model deployments on AWS and GCP.

Design, build, and deploy ML and generative models as services across AWS and GCP. Lead model lifecycle, MLOps, CI/CD, IaC, data pipeline architecture, and mentor junior engineers while collaborating with data, cloud, and business partners.

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

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

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.

Design and develop end-to-end AI capabilities in NLP, optimize ML models and systems, and collaborate with cross-functional teams.

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

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.

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

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.

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.

Design, develop, deploy, and maintain GenAI-based cloud applications and platforms. Lead solution design, coding, testing, integration and troubleshooting in containerized, Kubernetes-managed environments. Mentor engineers, ensure security and compliance, drive adoption of new technologies, and collaborate across teams to deliver scalable, cloud-native AI/ML services.

Design, develop, and deploy advanced machine learning and deep learning models; lead research, architecture, and productionization of AI solutions; mentor teams; monitor and optimize model performance; collaborate with cross-functional stakeholders to align AI/ML systems with business requirements.

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.