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.

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.

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.

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, 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 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, 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 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.

Design, build, deploy, and monitor ML models at scale. Collaborate with DS, QA, infra, and engineering teams to productionize and maintain ML services, optimize production code, and evolve solutions using modern ML practices.

Design, develop, and deploy machine learning models for predictive analytics, NLP, and computer vision. Preprocess and structure large datasets, implement scalable production deployments, monitor model performance, and collaborate with cross-functional teams to integrate AI-driven features while researching and applying best practices.

Lead full lifecycle development of L2/L3 embedded RAN protocol stacks: design, coding, unit testing, debugging, and integration. Implement and enhance MAC/RLC features, analyze logs, debug in Linux, and support product releases. Apply AI/ML concepts for RAN optimization and develop/maintain solutions used by global customers.

Deploy and maintain computer vision models in production: monitor failures, diagnose issues, propose algorithmic or data fixes, curate datasets, validate results for customers, and adapt SOTA architectures into efficient, impactful solutions.

The AI/ML Specialist will design and implement AI/ML models, collaborate with data engineers, ensure model explainability, and communicate technical concepts to stakeholders.

Lead implementation of Python-based backend integrations and data pipelines to deploy LLM/NLP/RAG solutions for enterprise clients. Configure cloud deployments, ensure reliability and observability, troubleshoot production issues, and collaborate with Customer Success, ML, and engineering teams. Mentor juniors and document implementation playbooks.

Lead design and production of face recognition and biometric CV systems. Build, train, optimize models (PyTorch/TensorFlow/JAX), own end-to-end ML pipelines (Airflow), ensure fairness, deploy low-latency inference on AWS, and mentor engineers.

Design, build, train, deploy, and support enterprise-scale AI/ML solutions including LLM-based, multimodal, and geospatial models for document understanding, legal entity extraction, and automatic generation of geo-tagged polygons integrated with GIS platforms.

Build and ship web-based machine learning features using Python or NodeJS, implement NLP models, integrate and manage data with NoSQL stores (DynamoDB), develop on AWS, work in agile teams, and collaborate cross-functionally to deliver customer-ready product features.

Seeking an AI/ML Engineer to design, develop, and deploy AI and machine learning solutions, using state-of-the-art models and technologies.