Top Machine Learning Jobs in India

Define and implement evaluation frameworks and automated test pipelines for agentic AI (MCP tools and Skills). Test tool invocation, multi-step workflows, and mitigate failure modes (hallucination, misuse, latency). Produce evidence for PTB/PTO, partner with ML engineers to embed testability, and shape long-term quality and governance for federated MCP contributions.

Design, build, and operate agentic AI platform components (MCP servers, Skills, QAS). Implement agent orchestration, tool‑calling, secure API wrappers, and RAG-based integrations. Collaborate with product teams, QA, and SRE to ensure governed, production-ready deployment and developer experience for enterprise AI agents.

Design, implement, deploy, and maintain large-scale recommendation and ML systems. Perform data analysis, experimentation (A/B testing), collaborate with product teams, monitor production systems, and apply MLOps practices using cloud and Kubernetes.

Manage and operate the Databricks Lakehouse and MLOps platform, ensuring availability, security, governance and cost optimisation. Lead incident/change/service management, define MLOps frameworks and CI/CD for model lifecycle, collaborate with engineering and data science to deploy and monitor models, and manage vendors and platform roadmap.

Design, develop, and deploy LLM-powered and Generative AI solutions including RAG pipelines, embeddings, prompt engineering, LangChain workflows, and agent systems; build AI-backed APIs and integrate models; handle data processing, vector databases, containerized deployments, basic MLOps, and implement RBAC/authentication and secure coding practices.

Architect, develop, and enhance AI/NLP systems and platform components. Design and customize machine learning and deep-learning models, implement NLP pipelines, work with Spark and common ML frameworks, deploy on AWS/Linux, and collaborate using Agile, Git/SVN, and Jira.

Work on large-scale machine learning projects for personalization, fraud detection, and NLP using big data systems. Research, design, and implement algorithms for classification, regression, and collaborative filtering, and communicate results to stakeholders.

Design, fine-tune, and optimize LLMs (LLaMA, OpenAI GPT) using LoRA and quantization; build RAG pipelines with FAISS and hybrid retrieval; develop autonomous agents with LangChain/ADK; implement ML/DL models in PyTorch and TensorFlow; build scalable APIs (FastAPI/Flask) and deploy on AWS/Azure; add guardrails, monitoring, evaluation, and performance optimization; containerize and manage deployments with Docker and Git.

Design, develop, and deploy production-grade ML solutions and Digital Twin models for smart manufacturing. Build predictive/prescriptive models (predictive maintenance, anomaly detection, throughput, scheduling), create feature and streaming pipelines, implement MLOps (training/deployment, model registry, drift monitoring), collaborate with engineers and SMEs, support plant visits, and ensure scalable, monitored AI solutions for industrial operations.

Develop and deploy LLM-based AI solutions and RAG applications using JavaScript/Node.js. Build AI agents, REST APIs, vector search integrations, and cloud-native microservices. Collaborate on prompt engineering, use cloud and container technologies, and support enterprise-scale AI deployments.

Seeking a Machine Learning Engineer to work on workforce development and artificial product development, applying machine learning and deep learning concepts.

Design and implement agentic architectures for humanoid robots, collaborating with teams to enhance performance, safety, and reliability through advanced methodologies.

Design, build, and deploy ML models for fraud, risk, and intelligence products; translate business problems into data solutions; develop anomaly detection and graph-based methods; own end-to-end model development, deployment, and monitoring; run experiments and manage large multi-country datasets; partner cross-functionally to ship privacy-safe data products and explain model outputs to technical and non-technical stakeholders.

Design, build, and deploy scalable AI/ML solutions including predictive models, recommendation systems, forecasting, anomaly detection, and Generative AI applications (LLMs, RAG). Implement MLOps (CI/CD, monitoring, versioning), production model deployment, data preprocessing, feature engineering, and collaborate with cross-functional teams to ensure reliability, scalability, and compliance.

Lead design, development, and deployment of enterprise AI/ML solutions including AI agents, LLM integrations, MLOps/CI-CD, AWS-based data pipelines, production model serving and APIs, and integrations with enterprise systems. Provide technical leadership and mentor engineering teams.

Design, develop, and deploy ML models and pipelines; preprocess data and engineer features; implement MLOps (Docker, Kubernetes, CI/CD); collaborate with stakeholders to translate business problems into AI solutions; monitor and optimize model performance and maintain documentation and governance.

Lead design, operation, and reliability of large-scale AI/HPC clusters. Manage day-to-day operations, incident response, automation, performance tuning, scheduler and storage optimization, and collaborate with researchers and global teams to improve GPU-accelerated infrastructure.

Design, train, optimize, and productionize deep learning models for speech/audio and computer vision. Build scalable training pipelines, perform model inference optimization, integrate models into production services, monitor performance, and collaborate with software and product teams to deliver low-latency, reliable ML features.

Design and validate performance models, optimize and debug kernel microcode and compiler algorithms, analyze runtime and cluster performance, and build tools to visualize performance data for ML/LLM inference on the Cerebras Wafer Scale Engine.

Design, build, and deploy AI systems for data loss prevention: experiment with NLP/LLM architectures, fine-tune models, implement real-time inference, develop Python and Go services, automate training/deployment, and participate in on-call operations to maintain production model accuracy and reliability.

Lead end-to-end design, build, deployment and iteration of AI-agent solutions. Embed with clients to map workflows, architect multi-agent systems, integrate enterprise systems and RAG pipelines, implement guardrails and observability, and liaise with non-technical stakeholders to drive adoption.

Lead and manage a team of ML engineers, drive AI service delivery, and oversee production model deployment while fostering a high-performance culture.

Build and maintain scalable ML pipelines and infrastructure, integrate LLMs, manage model lifecycle with MLFlow, collaborate in agile teams, write high-quality code, perform code reviews, deploy and monitor models in cloud environments.

Build, deploy, and maintain scalable ML pipelines and production models. Integrate LLMs into applications, manage model lifecycle with MLFlow, collaborate with data scientists and engineers, participate in Agile/Scrum, ensure code quality through reviews and CI workflows, and resolve infrastructure and deployment issues.

Lead and manage AI/ML projects and teams for KPMG India, delivering technology-enabled services to national and international clients while leveraging global network and local market knowledge.