Top Machine Learning Jobs in India

The role involves simulation, modeling, performance analysis, and tooling for AI/ML workloads, focusing on hardware/software co-design in advanced computing environments.

As a Data Science Consultant, you will optimize software solutions, conduct data mining, mentor juniors, and tackle customer data quality issues using advanced ML techniques.

Design, develop, and maintain applications using modern languages, build data pipelines, and deploy ML models on Azure. Collaborate with teams to drive projects from concept to production.

Design, develop, and deploy AI/ML solutions using NLP, NLU, LLMs and generative models. Implement data processing, feature extraction, and training pipelines. Lead and mentor an AI engineering team, collaborate with cross-functional stakeholders, evaluate new AI tools, and ensure scalable, timely, high-quality product delivery.

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

As an ML Quality Engineer at Cerebras, you will ensure software quality across ML workloads by implementing testing methodologies, debugging issues, and automating workflows.

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.

The role involves building a scalable ML platform, managing the ML lifecycle, optimizing cloud resource usage, and leading teams in implementing ML solutions.

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.

As an ML Platform Engineer, you will optimize and manage LLM inference services, improve performance, monitor operations, and collaborate with teams for production-grade deployments.

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 Machine Learning Engineer will deploy, maintain, and monitor ML models, design ML products, and collaborate with stakeholders while optimizing model performance in production environments.

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, 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.