Top Machine Learning Jobs in India

The AI/ML Architect designs, builds, and deploys scalable AI/ML solutions, collaborates with teams, and writes production-grade code.

As a Machine Learning Engineer at Level AI, you'll work on NLP challenges, develop scalable solutions, collaborate with teams, and innovate using cutting-edge ML techniques.

Lead strategy and development of AI/ML engineering solutions, enhancing decision-making and automation across the enterprise. Oversee advanced analytics and mentoring of data science teams.

Lead end-to-end ML solutions: define use cases, design data models and features, build and train models (classical, deep learning, NLP/LLM), productionize with MLOps, monitor performance, integrate modern AI tooling (embeddings, vector search), and mentor junior engineers.

Lead full ML lifecycle to design, build, and deploy AI/ML (including Agentic and LLM) solutions for enterprise-scale reconciliation. Analyze large financial datasets, define model roadmaps, integrate models into production, ensure scalability, manage risks, and communicate results to senior stakeholders.

Lead end-to-end data science projects applying machine learning and statistical modeling to deliver actionable insights. Collaborate with cross-functional teams, mentor junior data scientists, and drive methodological innovation to align analytics with business objectives.

Lead and grow an ML engineering team building content knowledge and discovery capabilities. Define ML roadmap, provide architecture and MLOps guidance, integrate generative AI and vision models, partner cross-functionally, and deliver scalable production ML solutions that improve search, recommendations, and ad experiences.

The Senior Machine Learning Engineer will design, develop, and deploy AI systems, focusing on Generative AI and LLM integration across various business operations, ensuring high performance and scalability.

Build and evaluate applied NLP and LLM systems for clinical text (oncology). Develop prototypes and production components for extraction, RAG, prompts, fine-tuning, and evaluation. Collaborate with clinical data and evaluation teams, write tested Python code, analyze model failures, and improve model robustness and reliability.

Lead design, development, fine-tuning and deployment of generative AI and LLM-based solutions. Build agent architectures, RAG-enabled NLP and multimodal pipelines, integrate vector DBs and LLM APIs, collaborate cross-functionally, and research state-of-the-art GenAI and autonomous agent techniques.

Design, build, and deploy production Generative AI and Agentic AI solutions on Azure. Lead end-to-end projects including RAG and agent orchestration, create architecture, mentor engineers, collaborate with business and architecture teams, and contribute to knowledge graph and data pipeline strategies.

Design, develop, fine-tune, and productionize ML and Generative AI/LLM solutions. Build end-to-end ML pipelines (data ingestion, modeling, deployment, monitoring), implement RAG and vector DB architectures, apply prompt/context engineering and MLOps best practices, and collaborate with cross-functional teams to optimize models for performance, latency, scalability, and cost.

Deploy and productionize ML models by building MLOps platform components, managing artifact versioning and serving, ensuring monitoring/observability, containerized deployments, cloud-native integrations, and collaborating with data and cloud engineers to scale and maintain model lifecycle and performance.

Manage the Goals Optimization Engine product lifecycle for digital wealth solutions. Conduct research, define requirements and user stories, partner with engineering and data teams, evaluate AI and emerging fintech opportunities (digital assets, blockchain), coordinate deployments and compliance, support testing and adoption, and monitor product performance and roadmap.

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.

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.

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.

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.

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, 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 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.

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.