Top Machine Learning Jobs in India

Design, build, and optimize scalable ML systems focused on Generative AI and LLMs. Fine-tune and deploy foundation models, lead research and prototyping, and implement end-to-end ML pipelines and APIs. Collaborate with data science, MLOps, and product teams to integrate AI capabilities into production.

Design, implement, and deploy LLM and ML solutions. Build and maintain ML pipelines, optimize GPU training and inference, implement A/B testing, monitor models, and collaborate with cross-functional teams to deliver scalable AI products.

Design and implement advanced prompt engineering strategies (ToT, CoT), fine-tune and deploy foundational models using Amazon SageMaker and AWS Bedrock, build and optimize ML training/inference workflows, manage identity with JumpCloud, monitor and refine model performance, and collaborate with cross-functional teams to integrate AI solutions.

Build and scale production-grade ML frameworks and decision systems for lending; design ML infrastructure, manage data and model lifecycles, enable multi-tenant analytics and agentic AI capabilities, monitor production performance, and document and propagate best practices.

Lead end-to-end ML lifecycle: design, build, deploy, and monitor LLM and ML solutions for underwriting. Architect scalable pipelines, optimize GPU and training workflows, implement MLOps/LLMOps practices, productionize RAG/Transformer models, and collaborate cross-functionally to drive product impact.

Design, fine-tune, and productionize LLM-based conversational and retrieval systems. Build scalable data pipelines and indexing, apply prompt engineering and few-shot learning, design APIs, run A/B tests and KPI monitoring, collaborate with engineering teams, and ensure data privacy, ethical AI, and secure, maintainable deployments.

Design, develop, and productionize machine learning and deep learning models, build scalable data and embedding pipelines, implement RAG/LLM-based applications using LangChain/LlamaIndex, integrate models as APIs, and optimize model performance and scalability for product integration.

Build and deploy production-grade computer vision and generative AI systems for product imaging and content pipelines. Tasks include detection, segmentation, depth/pose estimation, diffusion/GAN fine-tuning, automated catalog processing, recommendation models, scalable inference, CI/CD for models, monitoring, and rapid prototyping with state-of-the-art models. Own end-to-end ML features and optimize for production performance and cost.

Design, implement, evaluate, deploy, and monitor AI/ML and deep learning solutions. Preprocess large datasets, optimize models, deploy to cloud (AWS/Azure/GCP), and document results while collaborating with engineers and stakeholders.

Design, train, and productionize state-of-the-art object detection systems. Build robust training pipelines, perform error analysis and ablations, optimize inference for latency and memory, and deliver production services with Docker, CI/CD, and APIs. Implement testing, monitoring, model versioning, and retraining pipelines. Rapidly prototype research ideas into deployable components and maintain clear technical documentation.

Lead development and deployment of ML/AI solutions: build models (classification, prediction, anomaly detection, NLP), prototype ML products, prepare and pipeline data, collaborate with product and engineering, deliver insights, and mentor team members.

Design, develop, and deploy AI/ML models for clinical decision-making and operational efficiency, integrating AI capabilities into SaaS platforms.

Lead development and production deployment of AI/ML models focused on personalization, recommendations, segmentation, and predictive analytics. Collaborate with data engineering and development teams to ensure data readiness, integrate models into production (real-time systems like Kafka), apply AIOps to monitor performance, and mentor an AI/ML team.

Build and deploy BERT-based NER for document processing: ingest PDFs, perform layout analysis, develop human-in-the-loop workflows, optimize data pipelines, train and deploy models, and integrate with document processing services.

Design, develop, deploy, and maintain AI/ML solutions (NLP, CV, Generative AI). Build GenAI and agentic systems, implement RAG and vector-store integrations, optimize code, validate feasibility, support testing and UAT, monitor deployed solutions, and mentor COE members while coordinating with analysts and SMEs.

Lead design, development, and deployment of production-grade ML models, AI agents, and serverless data pipelines on AWS. Architect ML workflows, MLOps/CI-CD, data ingestion and model-serving infrastructure, integrate external systems, mentor engineers, and translate business needs into scalable AI solutions.

Design and implement ML and GenAI solutions including anomaly detection, NLP chatbots, real-time streaming analytics, Lambda event-driven workflows, Step Functions orchestration, REST API integrations, and integrations with ticketing and observability systems.

As a Machine Learning Engineer Intern, you will assist in developing computer vision algorithms for item authentication and fingerprinting, collaborating on model deployment, and contributing to code quality and documentation.

Build and operate production ML/LLM systems: design MLOps platform capabilities, standardized pipelines, model registry and artifact lineage; deploy using containers/Kubernetes and CI/CD; implement observability, SLOs, incident response, model/data monitoring, security controls, and enable teams with documentation and best practices.

Design, build, and deploy ML and generative AI applications using LLMs and RAG systems. Develop scalable APIs, manage AI pipelines and vector search, handle structured/unstructured data, train and evaluate models, and integrate AI capabilities into applications using cloud and deployment tools.

Design, build, and deploy generative AI solutions including RAG pipelines, agentic systems, and fine-tuned language models. Lead end-to-end AI projects from prototyping to cloud deployment, collaborate with cross-functional teams, and evaluate emerging AI technologies while ensuring scalable, production-grade engineering and ML ops practices.

Lead end-to-end development, deployment, and monitoring of scalable AI/ML solutions on Azure. Build forecasting, recommendation, optimization, LLM/RAG and agentic workflows, integrate APIs and data products, apply MLOps, ensure governance, and translate business needs into measurable analytics outcomes while collaborating with stakeholders.

Design and deliver ML solution architectures in healthcare. Lead model development, deployment and operationalization with cross-functional teams, communicate insights to clients, mentor senior staff, drive technical discussions, and identify sales opportunities.

Lead end-to-end development and deployment of deep learning and computer vision models for driver and environment safety. Collaborate with product teams, optimize distributed training and data workflows, and implement cloud/edge ML services.

Lead the design and deployment of advanced machine learning models, mentor junior staff, and drive innovation in AI technology at Ema.