Top Machine Learning Jobs in India

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.

No specific day-to-day responsibilities or technical requirements are listed. Role is a Machine Learning Engineer in the Digital Science job family at Solventum; applicants must provide education and work history when applying. Posting mainly contains company, terms, and application instructions.

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.

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.

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.

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.

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.

Design and implement agentic AI workflows and LLM-powered applications, build backend APIs and microservices, implement RAG pipelines, support ML model development and training, containerize and deploy services, and collaborate in an agile SDLC.

Design, build, and own content knowledge platforms using Generative AI and Vision models. Implement ML models for entity matching, deduplication, summarization, tagging, visual similarity, and data-quality checks. Deploy production ML pipelines, define KPI measurement frameworks, and collaborate cross-functionally to deliver scalable, secure, and high-quality user-facing systems.

Design, build, and productionize AutoML and AI/ML systems. Implement containerized, cloud-native pipelines, distributed training, model evaluation, hyperparameter optimization, and knowledge-graph architectures. Collaborate cross-functionally and communicate technical concepts to business stakeholders.

Support, analyze, validate, and calibrate market and credit risk models. Investigate market data inputs and anomalies, perform backtesting and model calibration, interpret model outputs, and collaborate with quant managers, risk teams, and developers to support risk technology platforms and publish validated results.

Design, develop, deploy, and maintain ML/AI solutions for LMS and student portals. Build recommendation engines, chatbots, predictive analytics, and data pipelines. Integrate models with web apps, support UAT, troubleshooting, documentation, and coordinate with product, tech teams, and vendors to ensure reliable, secure, and scalable ML systems.

Lead architecture and hands-on implementation of production-grade Generative AI and agentic systems. Build and deploy LLM-based solutions (RAG, embeddings, agents) on cloud (primarily GCP/Vertex AI), ensure reliability, latency, cost, observability, and governance, and advise clients while mentoring engineering teams.

Develop, deploy, and scale end-to-end analytical solutions for the Vapor Phase Drying process. Build ML models and data pipelines, work with dashboards and model monitoring, use Python and ML libraries, leverage Azure data and ML services, apply time-series forecasting, collaborate cross-functionally, and ensure regulatory and internal compliance.

Lead development of generative AI solutions that enhance business processes, mentor teams, and innovate AI technologies in finance.

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

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 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 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, prototype, and deploy Generative AI solutions (RAG, fine-tuning, embeddings) using LLMs, vector databases, and cloud AI platforms. Collaborate with sales, product, and engineering to translate requirements into technical roadmaps, deliver demos/SoWs, and provide MLOps and governance guidance.

Work with data scientists and senior ML engineers to build, train, and deploy machine learning models. Perform data preprocessing, feature engineering, model evaluation, and implement solutions using Python and common ML libraries. Follow version control and software engineering practices; exposure to cloud platforms is a plus.

Design, implement, optimize, and deploy ML and NLP models into production. Perform data preprocessing, experiments, analysis, and reporting. Maintain model documentation, apply MLOps/CI-CD, and communicate findings to stakeholders while staying current on AI research and ethics.

Lead AI architecture and strategy for a next-generation cybersecurity platform: design AI runtime, RAG/agentic workflows, model selection and fine-tuning, trust and safety guardrails, co-design data/feature stores and vector retrieval, and move research into production at scale.