Top Machine Learning Jobs in India

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.

Lead the architecture of physical implementation for high-speed CPU designs, focusing on automated ML-driven methodologies and PPA optimization, while interacting with EDA partners.

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.

The Machine Learning Associate Advisor will design and implement Gen AI/ML applications focusing on embeddings and vector databases, collaborate with teams, and ensure data quality throughout the AI/ML lifecycle.

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

The Machine Learning Engineering Manager will lead a team of ML engineers, defining technical direction and managing ML system development and production, while ensuring team productivity and communication across functions.

As a Senior ML Engineer at Kensho, you will develop, deploy, and evaluate ML systems, working with LLMs and data retrieval to enhance AI products.

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.

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.

Lead design, development, and deployment of production-ready ML/AI systems using Python, PyTorch, LLMs and related tools. Drive model research, tuning, monitoring, cross-functional integration, and mentor junior engineers while prioritizing practical business impact.

Work on ML/DL and computer vision models for aerospace and defense (missiles, rocket telemetry, avionics). Train, test, and improve models; prepare and clean text/image/sensor datasets; build lightweight demo APIs with FastAPI/Flask; document experiments and collaborate with mentors on real-world avionics systems.

Design and implement end-to-end AI/ML systems for robotic perception and control: research and build CV modules, train and benchmark models, prototype in simulation, transfer to real robots, create preprocessing/postprocessing pipelines in Python/C/C++, and optimize for real-time edge deployment.

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.

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.

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.

Lead the development of scalable enterprise software and AI/ML solutions for capital markets, ensuring compliance and high performance. Oversee the engineering team, define technical roadmaps, and drive innovation in software applications.

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

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.

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

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.

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.