▸ 3–5 years of professional experience in AI/ML engineering, applied research, or related software roles.
▸ Strong proficiency in Python for building production-grade AI/ML systems and microservices.
▸ Demonstrated experience building and fine-tuning ML models using TensorFlow, PyTorch, or scikit-learn for production-scale deployments.
▸ Hands-on experience with Model Context Protocol (MCP) — building MCP servers and clients, and integrating them within agentic or LLM-powered systems for standardised tool and data-source connectivity.
▸ Practical exposure to LLM frameworks and integration: LangChain or LlamaIndex for orchestration and RAG pipelines, and direct API integration with OpenAI or Anthropic (prompt engineering, tool use, embeddings).
▸ Experience with vector databases — Pinecone, Weaviate, Qdrant, or pgvector — for semantic search and RAG workflows.
▸ MLOps proficiency: experiment tracking (MLflow, Weights & Biases), CI/CD for ML, model registries, and feature stores.
▸ Solid grounding in statistics, linear algebra, and optimisation fundamentals essential for ML model development and evaluation.
▸ Experience designing autonomous AI agents and multi-agent workflows using LangGraph, AutoGen, or CrewAI with tool-use and function-calling patterns.
▸ Experience with Docker and Kubernetes for containerisation, orchestration, and scalable AI/ML system deployment.
▸ Proficiency in CI/CD pipeline design for AI/ML systems, including automated testing, model validation, and deployment tooling.
▸ Deep understanding of microservices design patterns, distributed systems, and RESTful API design for building scalable AI/ML infrastructure.
▸ Deep understanding of microservices design patterns, distributed systems, and RESTful API design for building scalable AI/ML infrastructure.
Pay: ₹1,200,000.00 per month
Work Location: In person
