Top Machine Learning Jobs in India

Design and validate performance models, optimize and debug kernel microcode and compiler algorithms, analyze runtime and cluster performance, and build tools to visualize performance data for ML/LLM inference on the Cerebras Wafer Scale Engine.

Design, build, and deploy AI systems for data loss prevention: experiment with NLP/LLM architectures, fine-tune models, implement real-time inference, develop Python and Go services, automate training/deployment, and participate in on-call operations to maintain production model accuracy and reliability.

Lead and manage a team of ML engineers, drive AI service delivery, and oversee production model deployment while fostering a high-performance culture.

Build, deploy, and maintain scalable ML pipelines and production models. Integrate LLMs into applications, manage model lifecycle with MLFlow, collaborate with data scientists and engineers, participate in Agile/Scrum, ensure code quality through reviews and CI workflows, and resolve infrastructure and deployment issues.

Lead and manage AI/ML projects and teams for KPMG India, delivering technology-enabled services to national and international clients while leveraging global network and local market knowledge.

Research, prototype, and integrate LLMs and NLP solutions into business use cases. Experiment with and fine-tune ML/DL models, optimize performance, build data pipelines, and collaborate with cross-functional teams to deploy LLM-based applications.

The ML QA Lead ensures quality in Cerebras software across ML workloads, focusing on testing accuracy, performance, and usability. Responsibilities include driving software quality, automating workflows, debugging issues, and collaborating across teams.

The Senior Data Scientist will design and maintain data pipelines, optimize machine learning models, and collaborate with cross-functional teams to deliver impactful data-driven solutions.

Create and curate AI/ML and data science course content, deliver instructor-led and virtual training, mentor candidates on project work, and stay current with emerging AI technologies. May require travel.

The role involves developing data-driven applications and APIs using Python, building chatbots with LLMs, and deploying scalable solutions.

Design and deliver end-to-end GenAI and ML solutions on AWS: build SageMaker pipelines, fine-tune LLMs (Llama2), implement RAG with vector stores (OpenSearch/Elasticsearch), develop LangChain-based LLM apps, perform prompt engineering, model evaluation and optimization, and collaborate with cross-functional teams to integrate models into production.

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.

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.