Top Machine Learning Jobs in India

Senior Databricks MLOps Engineer responsible for automating the end-to-end ML lifecycle: CI/CD, packaging, deployment, scheduling, monitoring, and reproducible experiments. Build frameworks and pipelines, integrate embedding/LLM components (RAG, vector DBs, LangChain), optimize ML workloads, and collaborate with data scientists and platform teams to productionize models for Claims Payment Integrity.

Design, build, and deploy production ML and NLP systems (including transformer and retrieval-based models). Work across the ML lifecycle: data exploration, feature engineering, training, evaluation, deployment, monitoring, and retraining. Collaborate with Product, Engineering, and Analytics to translate requirements into scalable, observable ML solutions and contribute to GenAI/RAG/agentic AI initiatives.

Design, build, and maintain end-to-end ML and data solutions: analyze and preprocess client data, implement ML and deep learning models, deploy LLM/GenAI solutions, build data pipelines, monitor model performance, integrate APIs, collaborate with cross-functional teams, and communicate insights to stakeholders.

Build and deploy ML models and systems powering autonomous AI agents for loan servicing and collections. Train and fine-tune LLMs, design evaluations and monitoring, tune prompts, integrate APIs, debug real-time issues, and own features from definition through production.

Design, train, fine-tune, and deploy NLP and generative AI models (including LLMs) and conversational agents. Implement MLOps practices for production deployment, monitor model performance, iterate improvements, and integrate AI solutions on cloud platforms (AWS/GCP) while collaborating with cross-functional teams.

Design, build, and deploy production Generative AI solutions (chatbots, RAG, agentic workflows) integrated with EDA/HPC infrastructure. Automate operations, troubleshooting, and orchestration across Linux, cloud (AWS), storage, networking, schedulers, and monitoring systems. Implement vector DBs, APIs, CI/CD, monitoring, and governance for enterprise AI applications.

Design, build, and deliver enterprise-grade Generative AI and AI/ML platform capabilities: reusable services, APIs, microservices, RAG and agentic solutions, model lifecycle tooling, inference and monitoring, event-driven streaming with Kafka, CI/CD and DevOps automation, and platform observability to accelerate AI adoption across business domains.

Design, develop, and deploy scalable AI/ML systems using Python and modern frameworks. Build LLM-based solutions, RAG pipelines, AI agents, and APIs. Handle data preprocessing, feature engineering, model training, evaluation, optimization, and production monitoring. Integrate models with web/mobile/backend systems, work with vector databases and embeddings, optimize inference, participate in architecture, mentor junior engineers, and communicate with clients and stakeholders.

The AI/ML Tech Lead engages with clients to design and manage scalable AI/ML solutions, overseeing the complete delivery lifecycle and leading cross-functional teams.

The Product Manager will define and drive AI-powered statistics for life sciences, collaborate across teams, engage with customers, and support market differentiation strategies.

The Senior Engineer will develop and deploy machine learning models and workflows, mentor junior engineers, and collaborate with cross-functional teams to improve patient care through innovative solutions.

The Senior Machine Learning Engineer will design, train, and deploy ML models for optimizing revenue in advertising, taking ownership of product development and leading the ML discipline.

Design and deploy AI/ML solutions on Snowflake, build ML pipelines and POCs using SQL/Python/APIs, advise customers and systems integrators, enable teams via best practices, collaborate with Product/Engineering, and travel to customer sites (~25%).

Build and productionize ML systems for LLM-powered developer-productivity features: evaluation/backbone, model routing and inference economics, scoring and signal quality, MLOps (versioning, rollouts, monitoring), and collaborate across frontend/backend and data pipelines to maintain quality and low-latency serving.

Build and support ML/DL systems end-to-end for manufacturing use cases: problem framing, experimentation, and production deployment. Work on domain-specific problems like manufacturability analysis, predictive maintenance, and automated part routing to ship solutions running in live production.

Design, build, and operate scalable backend services and APIs to deliver AI-powered workforce management features. Integrate LLMs, agents, and retrieval/RAG systems into production, ensure observability and reliability, contribute to CI/CD and deployment automation, troubleshoot production issues, and mentor junior engineers.

The role involves optimizing compiler performance, designing features for a high-performance architecture, and collaborating with hardware architects and model developers for effective ML inference.

Design and deploy ML pipelines, develop CI/CD workflows, implement monitoring systems, collaborate with teams, and write scalable Python code for ML applications.

As a Machine Learning Engineer, you will develop and maintain pipelines, deploy models as APIs, optimize ETL processes, and integrate ML with backend systems. Your focus will be on ensuring model reliability and data consistency in production environments.

Design, implement, and lead large-scale generative AI and ML systems (LLM fine-tuning, RAG, agents, Graph ML). Build REST APIs, deploy with Docker/Kubernetes, optimize big-data workflows, mentor engineers, and operate ML services on cloud platforms.

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.

Develop ADAS computer vision, radar and sensor-fusion algorithms (stereo/mono camera, LiDAR, RADAR). Work on calibration, 3D reconstruction, SLAM, object tracking, filters, and production-grade deep learning models. Deploy and optimize CV/ML solutions on embedded/edge platforms using C/C++, Python, MATLAB, DL frameworks, and relevant toolchains.

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.

Build evaluation systems, model routing experiments, and scoring pipelines for LLM features. Work across MLOps, model versioning, monitoring, and product-facing engineering to ensure reliable, calibrated AI feature quality.