Top Machine Learning Jobs in India

Develop and deploy production-grade data science, generative AI, and agentic AI solutions using Snowflake and Cortex AI. Build intelligent data-driven applications with Python, SQL, Snowpark, LLMs, RAG, prompt engineering, vector search, embeddings, and AI agents. Integrate machine learning solutions with enterprise data platforms and develop, deploy, and maintain ML models.

Design, develop, and deploy production AI/ML solutions using machine learning, LLMs, RAG, AI agents, computer vision, NLP, and predictive analytics. Prepare datasets, build APIs and enterprise integrations, monitor model performance, evaluate emerging technologies, and document architectures and deployment pipelines. Collaborate with product managers, engineers, and business stakeholders to translate operational challenges into scalable AI systems across manufacturing, supply chain, recycling, and enterprise workflows.

Design and implement test plans, write test code, validate software/SDKs and AI models for geospatial AI products. Troubleshoot issues, collaborate with data scientists, create documentation, samples, tutorials, and develop pretrained geospatial AI models for imagery, 3D, and text.

Design, develop, and deploy scalable AI/ML models and intelligent agent systems. Architect LLM-powered autonomous and multi-agent workflows using frameworks such as LangChain, AutoGPT, and CrewAI. Integrate AI solutions with enterprise systems, optimize cloud deployments, and apply MLOps and CI/CD practices. Collaborate with product, engineering, and business teams while promoting responsible AI, fairness, transparency, and knowledge sharing.

Design and implement scalable machine learning, generative AI, prediction, and optimization solutions for Nike’s corporate functions. Build and improve MLOps platforms, data pipelines, and production applications while partnering with stakeholders and technology teams. Provide technical leadership, mentor engineers, influence technical strategy, and deliver robust solutions using cloud, data engineering, and modern ML technologies.

Implement and enhance machine learning and data analytics solutions using cloud-native AWS tools. Participate in customer discussions, troubleshoot infrastructure, contribute to solution architecture and proofs of concept, and monitor emerging AI/ML and cloud trends. The role requires collaboration, documentation, analytical problem-solving, and hands-on work with Python, SQL, PySpark, Spark, NLP, transformer models, statistics, and AWS machine learning services.

Design, build, and maintain scalable cloud data pipelines, databases, and analytics platforms using Azure and Databricks. Integrate industrial/time-series data, develop ETL/ELT and API-based integrations, define data models and governance, and support analytics, AI-driven solutions, predictive maintenance, and operational reporting in collaboration with cross-functional teams.

Build and operate a production AI engine for root cause analysis. Responsibilities include vector database design, embedding and ingestion pipelines, RAG retrieval, prompt engineering, structured output validation, confidence calibration, LLM evaluation, tenant isolation, and monitoring of retrieval quality, cost, and latency. The role requires strong Python development with FastAPI and Pydantic, production LLM experience, and familiarity with semantic search, SRE observability, and cloud infrastructure.

Lead end-to-end development of conversational AI and LLM systems, including model architecture, fine-tuning, RAG integration, voice API integration, cloud deployment, and performance optimization. Collaborate on data preparation and training workflows, implement MLOps practices, monitor deployed models, research emerging AI technologies, and mentor junior engineers.

Provide 24x7 operational support for public/private cloud infrastructure (AWS services), monitor availability, manage incidents to meet SLAs, support production deployments, ensure fintech compliance and security best practices, and communicate technical concepts to stakeholders.

Design, build, and deploy ML models and end-to-end pipelines (data ingestion, feature engineering, training, validation, serving). Implement MLOps practices, containerized deployments, model monitoring, and retraining workflows. Collaborate with cross-functional teams, optimize model performance for production, write production-quality code, participate in architecture decisions, and mentor junior engineers.

Partner with strategic enterprise customers to define GenAI strategy, design architectures using ChatGPT/OpenAI API, build prototypes to production, manage stakeholder relationships, scale best practices and notebooks, and provide high-signal feedback to Product and Research teams.

The role involves developing enterprise applications using Java and Spring, integrating AI features, mentoring junior developers, and collaborating with cross-functional teams.

Develop and maintain Python-based optimisation models and data pipelines for energy asset revenue analysis in wholesale electricity markets. Write production-quality code, run technical testing, document designs, use AI tools to accelerate development, and collaborate on deploying machine learning components. Knowledge of energy markets and power systems is a plus.

Own commercial strategy and operations for enterprise pharma and AI accounts: drive pilots-to-revenue, account expansion, pipeline management, pricing, forecasting, and deal strategy. Build cross-functional relationships, push deals through legal/procurement/IT, act as CEO counterpart to shape strategy and product roadmap, and be highly present in person with customers and at conferences.

Lead and architect end-to-end ML/AI solutions across multiple projects. Own model design, development, fine-tuning, deployment and integration into business applications. Optimize production ML systems for performance and scalability, mentor engineers, coordinate cross-functional teams, and ensure timely delivery of ML initiatives. Apply transformer fine-tuning and recommender/search techniques on cloud platforms.

Design, implement, optimize, and deploy machine learning models and pipelines for production. Perform data processing, feature engineering, model training, evaluation, and runtime tuning. Collaborate with engineers on integration and infrastructure, maintain technical documentation, participate in code reviews, and support sales/implementation teams with technical guidance.

Design, develop, and maintain UI and API automation for AI/ML and generative AI applications. Own end-to-end testing (functional, integration, regression), validate model outputs (accuracy, relevance, safety, hallucinations), build reusable frameworks, integrate tests into CI/CD, collaborate with engineering and product teams, and mentor junior engineers to promote automation-first AI quality practices.

Design, build, and scale generative AI and ML solutions including LLM fine-tuning, RAG pipelines, model training and deployment on AWS SageMaker/Bedrock, experiment tracking, and computer vision tasks. Work with vector databases, ML libraries and deep learning frameworks, and develop REST APIs and production integrations.

Lead technical vision and delivery of AI/ML and Generative AI solutions (LLMs, computer vision, forecasting). Mentor engineers, design architectures, build RAG pipelines, manage ML lifecycle, deploy and monitor models on AWS, and collaborate with stakeholders to translate business problems into scalable AI systems.

Build and operationalize on-premises large language models across multiple product use cases. Responsibilities include fine-tuning and training pipelines, embeddings and vector search, RAG architecture, model evaluation, inference serving, observability, Docker and Kubernetes deployment, knowledge graph integration, and agentic AI prototyping. The role also requires collaboration with engineering, product, data, QA, and platform teams, plus ownership of production model quality and reliability.

Define technical vision and architecture for enterprise AI and Generative AI platforms. Architect large-scale ML/LLM systems, lead cross-functional high-risk programs, establish governance and responsible-AI standards, and mentor senior engineers while partnering with leadership to prioritize AI investments and deliver production-scale capabilities.

Design, develop, and deploy machine learning systems focused on agentic and generative AI. Implement ML pipelines and MLOps practices, and build/optimize models (e.g., XGBoost) for production.

Design, build, and deploy AI/ML and Generative AI solutions for enterprise use cases. Build and maintain end-to-end ML pipelines, deploy models to production, and implement monitoring, observability, and model-drift detection. Work with APIs, vector databases, embeddings, and RAG-based applications, and collaborate with cross-functional teams to optimize scalable AI deployments.

Lead design, build, deploy, and monitor enterprise-scale AI/ML and Generative AI systems. Own end-to-end ML pipelines, scalable inference, vector search and RAG applications, MLOps, observability, governance, CI/CD, model lifecycle management, and production support. Mentor teams, collaborate with stakeholders, and optimize AI systems for scalability, latency, reliability, and cost.