Top Machine Learning Jobs in India

Design and optimize scalable data pipelines, maintain data models, ensure data quality, and collaborate with ML engineers. Mentor junior engineers.

Design, build, deploy, and maintain end-to-end AI/ML solutions (NLP, computer vision, predictive analytics). Improve data quality and scalability, run experiments, optimize models, and communicate insights to stakeholders.

Develop and productionize advanced ML and NLP models (including LLMs) to personalize marketing content. Explore text and image opportunities, build scalable prototypes from large datasets, design experiments (A/B, multivariate), collaborate with cross-functional teams, mentor peers, and present results to stakeholders to drive business impact.

Lead a data science team to design, build, and deploy ML models and scalable PySpark data pipelines on Azure. Oversee feature engineering, model lifecycle, monitoring, performance tuning, MLOps/CI-CD, and translate business needs into analytical solutions while presenting results to stakeholders.

Design, develop, and deploy AI/ML solutions from prototype to production, including traditional ML and modern LLM/generative/agentic systems. Build end-to-end pipelines, ensure model evaluation, fairness and reliability, collaborate with stakeholders, and meet healthcare regulatory and governance requirements.

Lead and scale global machine learning and computer vision teams across India and Europe, define ML strategy and roadmap, deliver production-grade training/inference pipelines and edge deployments, optimize performance and reliability, drive cross-functional collaboration, hire and develop senior talent, and establish operational rigor and metrics for continuous improvement.

Lead design, development, and delivery of AI/ML solutions. Build and optimize models, collaborate with cross-functional teams, mentor engineers, enforce best practices, and stay current with AI trends.

Lead the rebuild of Roku's relevance stack using state-of-the-art ML (LLMs, transformers, deep learning, bandits) for search, ranking, and monetization. Run AB tests, analyze KPIs, collaborate cross-functionally, scale ML systems and data pipelines, and mentor/hire engineers.

Design, build, deploy, and monitor end-to-end AI/ML systems. Curate and preprocess data, prototype models, define success metrics, integrate features into product infrastructure, and collaborate with cross-functional teams to improve user experience and care delivery.

Provide senior-level technical expertise to debug, optimise, and productionize GenAI and ML workloads on Databricks. Troubleshoot data and ML pipelines, LLM deployments, model serving, vector search, agent systems, monitoring, and cost/latency issues. Advise customers on MLOps/LLMOps, experiment tracking, model lifecycle, prompt engineering, retrieval/embedding systems, and collaborate with engineering and account teams to influence product and create documentation.

Architect and implement enterprise-grade MLOps/LLMOps on AWS (EKS-first), building scalable pipelines for training, validation, registry, deployment, monitoring, CI/CD, observability, lineage and governance; serve as technical authority, conduct architecture reviews, troubleshoot systems, and mentor engineering teams across projects.

Serve as a hands-on technical architect advising customers on designing, building, and deploying data science and ML workloads on Snowflake. Create POCs, implement ML pipelines using SQL and Python (or Java/Scala), apply MLOps best practices, collaborate with SIs, and enable customers through knowledge transfer and strategic guidance.

Lead ML architecture and solution design for NLP, computer vision, and generative AI projects. Define technical designs, review implementations, run POCs, guide developers, and ensure scalable, secure, and ethical deployments using cloud, MLOps, and model deployment pipelines.

Lead design and delivery of ML solutions across NLP, vision, and generative AI. Convert business needs into architecture and designs, guide developers, run POCs, ensure scalability, security, MLOps, and responsible AI best practices, and build/deploy AI agents and LLM-based systems.

Lead design and delivery of scalable ML and GenAI solutions (LLMs, RAG, agents). Build end-to-end ML pipelines, deploy and monitor models in production, perform deep analyses, and partner cross-functionally to drive product and GTM impact.

Design, build, and deploy scalable ML pipelines and production models (MLOps). Integrate and manage LLMs, track model lifecycle with MLFlow, collaborate in agile teams, perform code reviews, and ensure robust ML infrastructure and monitoring.

Design, build, and deploy machine learning models for a payments platform, overseeing end-to-end solutions and experimentation, while monitoring models in production.

The Director of Software Engineering will lead teams in developing highly available distributed systems, focusing on payment processing, AI solutions, and innovative capabilities, while ensuring product quality and team engagement.

The Senior ML Engineer will improve Avoma's Insights Engine using NLP and Deep Learning, developing algorithms, new models, and shipping them to production.

The role involves developing scalable AI and machine learning solutions, focusing on large language models and generative AI for healthcare applications, while ensuring compliance with ethical standards.

The Senior AI / ML Ops Engineer will design and operate Agentic AI systems, implement MLOps practices, and ensure production automation is secure and reliable.

The Machine Learning Engineer/Data Engineer analyzes large datasets, develops predictive models, and leads technical development while mentoring junior team members.

Design, build, and deploy LLM-based solutions on Azure (CPU/GPU). Research and implement pretraining, fine-tuning, alignment, and prompt engineering. Build scalable data pipelines and tooling, ensure production-quality Python code, containerize deployments (Kubernetes/Docker), optimize distributed low-latency architectures, and communicate progress with stakeholders.

Lead architecture and modernization of the CMDB to be AI-ready: assess gaps, design Databricks/Microsoft Fabric integrations with ServiceNow, introduce knowledge graphs and predictive intelligence, automate CI lifecycle, reconciliation, and observability, and lead a team of CMDB data engineers to maintain data quality and governance.

Design and build production-grade generative AI and ML solutions (LLMs, NLP, CV) including APIs, data pipelines, vector search, model optimization, and dashboards. Lead technical direction, ensure responsible AI practices, and collaborate across product and engineering to deliver scalable, secure, and auditable systems.