Top Machine Learning Jobs in India

The Machine Learning Engineer will develop and maintain AI/ML systems, collaborate with teams for integration, and ensure compliance with standards.

As an ML Research Engineer, you will adapt and optimize language and vision models for efficient inference on Cerebras hardware, focusing on advanced techniques for low-latency, high-throughput performance.

Lead design, development, and deployment of production-grade LLM and RAG-based AI solutions. Improve retrieval performance, build automated evaluation and validation frameworks, instrument pgvector retrieval KPIs, integrate metrics into CI/CD, and mentor mid-level engineers while driving technical reviews and best practices.

Design, build, and scale Azure ML solutions and data pipelines for LLMs, RAG, and agentic AI systems. Implement MLOps/DataOps practices, run A/B tests, monitor production models, and collaborate with stakeholders to deliver AI-driven decision support.

Build and optimize Angular front-end features that integrate AI/ML models and APIs. Collaborate with data scientists, backend engineers, and designers to implement responsive, secure UI components, visualize model outputs, and deploy AI-powered web functionality.

Lead AI/ML development initiatives: design algorithms, mentor engineers, manage development process, and deploy AI solutions to production.

Develop production-ready AI applications and LLM pipelines using Python and FastAPI. Implement embeddings, vector stores, and RAG; use GCP services (Vertex AI, Cloud Run/Functions, BigQuery, Cloud Storage). Apply advanced prompt engineering, orchestration frameworks (LangChain, LangGraph, CrewAI), integrate with enterprise platforms, and follow CI/CD and MLOps best practices.

Lead AI/ML development efforts: design algorithms and models, manage and mentor engineering teams, and deploy production AI solutions.

Develop and deploy cross-sell and upsell ML models (e.g., XGBoost) for credit-card portfolio growth and activation. Target segments/personas, recommend campaigns and merchants to drive spend, collaborate with product, operations and portfolio teams, present results to senior stakeholders, and manage project pipelines and documentation.

Develop and deploy production-ready AI applications and LLM pipelines using Python and FastAPI. Build data ingestion, inference, embeddings, vector stores, and RAG systems on GCP (Vertex AI, Cloud Run/Functions, BigQuery, Cloud Storage). Use orchestration frameworks (e.g., LangChain) and implement CI/CD, testing, and integrations while applying advanced prompt engineering and recommending new AI tools.

Lead and manage a data science team to design, build, and deploy ML, NLP, and GenAI solutions that drive sales growth. Develop scalable data pipelines, forecasting and incentive models, GenAI use cases, KPIs, and executive-facing analytics while collaborating across Sales, Marketing, Finance, and Technology.

Build distributed microservices and task-graph execution engines for LLM-driven workflows. Integrate multiple LLM providers and tools, implement gRPC/REST APIs, document ingestion and embedding pipelines, vector search knowledge bases, safe code-execution sandboxes, and contribute to MCP server implementations while maintaining containerized infrastructure and observability.

Design, build, and maintain ML systems for a multilingual dubbing platform. Develop automated validation, monitoring, and evaluation workflows. Optimize inference pipelines for performance, cost, and reliability, run experiments, and collaborate with product and engineering to productionize models.

Operate and own model lifecycle for defence and strategic deployments: design serving infra, CI/CD for model updates, monitoring/observability, evaluation/A-B testing, containerised serving for edge and air-gapped environments, collaborate on eval pipelines, create runbooks, and lead incident response for production model failures.

Build and deploy vision-language models across the full lifecycle: data pipelines, training/fine-tuning on GPU clusters, evaluation and benchmarks, inference optimization, production integrations, and client-facing solutions for document processing and visual search.

Lead design and delivery of end-to-end ML solutions for edge devices: architect, develop, optimize, validate, deploy models and pipelines; mentor a small AI engineering team; integrate AI systems with embedded teams and respond to client RFQs.

Design, develop, and optimize deep learning models (especially computer vision) end-to-end: data handling, model selection, training, evaluation, and deployment to edge devices. Use frameworks like TensorFlow/PyTorch, apply transfer learning, and optimize for resource-constrained platforms.

Design, develop, and deploy machine learning and deep learning models; build end-to-end ML pipelines including preprocessing, training, evaluation, and deployment; perform feature engineering on large datasets; deploy and monitor models on cloud platforms; collaborate with cross-functional teams to integrate AI solutions and retrain models as needed.

Design, build, and maintain AI platform and ML pipelines by assembling LLMs and agentic workflows, integrating assistants with systems, prototyping in low-code and code, writing tests and CI/CD, and iterating with stakeholders on domusAI, domiAI, domusDocs and multi-agent frameworks.

Build and deploy production-grade generative AI applications: design multi-agent systems and LangGraph workflows, implement advanced RAG with vector DBs, fine-tune and host LLMs, develop scalable FastAPI backends, optimize latency/cost, and containerize services for production.

Design, build, and scale production Generative AI systems: multi-agent workflows, advanced RAG pipelines, model fine-tuning and quantization, LLM integration and swapping, high-performance FastAPI backends, vector search and retrieval, containerized deployment, and monitoring for latency, cost, and reliability.

Design and build Python-based integrations, data pipelines, APIs, and AI-enabled workflows; deploy and maintain LLM/NLP/RAG capabilities in cloud environments; troubleshoot production issues; lead customer-facing implementations and mentor engineers to deliver scalable, reliable solutions.

Design and implement end-to-end machine learning solutions: analyze use cases, build statistical and ML models, prepare and enrich data, implement scalable PySpark models in Databricks, and collaborate with product and engineering to productionize analytics for personalization and business insights.

The role focuses on fostering a culture of innovation and growth while leveraging technology. It emphasizes a collaborative environment to achieve personal and professional excellence.

Lead design and delivery of simulation, optimization, and AI/ML solutions for container terminal operations. Build operational and strategic tools, coach junior team members, liaise with stakeholders, measure model impact, and iterate models to improve efficiency in yard, vessel, and truck operations.