Top Machine Learning Jobs in India

The Digital & AI/ML Engineer will implement AI pipelines, develop AI-powered applications, and collaborate on AI solutions to improve productivity.

Design, build and deploy AI applications for utilities. Responsibilities include working with LLMs, data pipelines, and ensuring system performance.

As a Staff Software Engineer, you will develop and deploy ML/AI systems, mentor junior engineers, ensure quality, and own the full machine learning lifecycle.

The role involves building and deploying core ML/AI systems, mentoring junior engineers, and ensuring model quality and efficiency. Responsibilities include overseeing the full machine learning lifecycle, implementing robust frameworks, and collaborating with cross-functional teams to improve services and components.

The Senior Machine Learning Engineer will build and productionize ML models and generative AI capabilities for Roku's Ad Data Activation, focusing on large-scale data platforms and improving automation and scalability.

Design and deploy AI/ML solutions utilizing Python, integrate OpenAI APIs, build applications with Streamlit, and leverage Snowflake for analytics. Responsibilities include development of ML pipelines, optimization of model performance, and collaboration with various teams.

As a Product Manager, you will drive product strategy, delivery, and feature prioritization for Enterprise Data initiatives, ensuring alignment between customer needs and business goals.

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

Design, develop, and deploy scalable production ML solutions across deep learning, LLMs, vision, and speech. Own end-to-end ML feature lifecycle, optimize performance and latency, build supporting APIs/microservices, implement MLOps best practices, and collaborate with product and leadership on technical strategy.

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.

Develop and deploy LLM-powered applications and workflows, implement RAG pipelines, and productionize AI systems integrating into enterprise workflows.

The Senior Machine Learning Engineer will design and maintain ML infrastructure, develop evaluation frameworks for LLMs, ensure system reliability, and mentor team members.

The role involves applied research in LLMs, fine-tuning models, evaluation frameworks, developing knowledge graphs, collaborating on AI solutions, and conducting experiments for model performance.

Build and operate end-to-end ML lifecycle systems: data processing, model training, low-latency serving, real-time feature pipelines, monitoring, A/B experimentation, and MLOps automation to deploy scalable recommendation systems.

The AI/ML Engineer will build and deploy GenAI applications, design agentic systems, develop AI backend services, and ensure responsible AI compliance.

Define AI/ML architectures, evaluate tools, drive POCs, mentor teams, and establish governance while ensuring scalability and security standards.

Design and build machine learning platforms and pipelines, lead model lifecycle infrastructure development, and mentor junior members while promoting best practices in ML operations.

Design, build, and operate production-grade RAG pipelines: ingestion, embedding, indexing, retrieval, reranking, and grounded answer generation. Optimize search quality, latency, cost, security, evaluation metrics, and observability while partnering across teams to deploy and continuously improve enterprise retrieval systems.

Seeking an Enterprise Architect with strong GenAI and AI/ML expertise to lead engineering teams and deliver AI-powered enterprise solutions.

The Enterprise Architect is responsible for designing AI-powered enterprise solutions and leading teams in implementing and deploying AI/ML applications across various environments.

Develop and optimize ML models for computer vision tasks, build data processing pipelines, and collaborate on robotics integration.

Design, build, and deploy production-grade ML systems (NLP and Generative AI), own MLOps and observability, solve complex unstructured data problems, mentor engineers, and collaborate cross-functionally to deliver scalable, reliable ML solutions.

The role involves building scalable AI solutions, architecting software systems, and integrating machine learning technologies to improve healthcare services. Responsibilities include developing AI pipelines, optimizing models, and ensuring compliance with AI ethics.

Design, develop, and implement ML models for search and recommendation systems. Collaborate with data engineers and scientists to integrate, enhance and scale ML solutions for personalization in various sectors.

The ML Engineer will design, implement, and maintain backend services, integrate ML models, and collaborate with product owners and teams for efficient project execution.