Top Machine Learning Jobs in India

The Associate Architect will lead the development and deployment of machine learning solutions on Azure, manage the ML lifecycle, and optimize existing models while providing technical leadership to project teams.

Lead a team of ~5 ML engineers to research, prototype, and deploy multimodal ML models for robotic inspection. Curate datasets, establish evaluation methods, fine-tune generative models (esp. vision), and improve model performance for anomaly detection and object classification within industrial sites.

As a Senior Machine Learning Engineer, you'll build LLM-powered agents, collaborate on experiments, support prompt engineering, and monitor AI features in production.

Lead the development of AI subsystems in healthcare, translating requirements into technical strategies for scalable AI/ML and computer vision solutions, ensuring quality and compliance.

Lead architecture and development of agentic AI solutions for optical networks: design agent orchestration, memory, tool integration, guardrails, and human-in-the-loop workflows; convert processes into autonomous/semi-autonomous automation; document designs and best practices for scalable delivery.

Develop and optimize machine learning infrastructure and models for ad technology. Collaborate with teams for data analysis and engineering.

Design, develop, test, and deploy scalable GenAI, ML, and deep-learning solutions. Build and optimize RAG pipelines and LLM-powered, agentic AI applications. Create REST APIs and backend services, preprocess and integrate structured/unstructured data, fine-tune and monitor models, implement semantic search, and ensure production reliability, security, and performance while collaborating with cross-functional teams.

Lead and grow an ML platform team (4→10–12), owning GPU compute infrastructure, data storage and pipelines, shared tooling, architecture, cross-functional partnership, and hiring while remaining hands-on.

The role involves designing and deploying machine learning models, ensuring model performance, and optimizing ML infrastructure on GCP, collaborating closely with data engineers and scientists.

Lead engineer responsible for end-to-end bring-up of ML models and frameworks on Cerebras CSX systems. Work across model translation, graph lowering, compiler optimizations, runtime integration, and performance tuning. Debug performance and correctness issues across model code, compiler IRs, runtime behavior, and hardware utilization. Prototype tooling and API improvements to accelerate future bring-ups.

The role involves leading AI ML initiatives, managing product development, ensuring adherence to software best practices, and overseeing project delivery in a global context.

Manage a team of AI Engineers and Data Scientists to deliver AI-ML solutions, ensuring quality, scalability, and alignment with business goals.

The AI/ML Consultant will design automation strategies for Order-to-Cash workflows, identifying improvement areas and delivering a comprehensive roadmap for implementation.