We are looking for a hands-on Senior AI/ML Engineer to design, build, and deploy production-grade Agentic AI and Generative AI solutions.

• Agentic system design:Architect and build single- and multi-agent systems with planning, tool use, memory, reflection, and human-in-the-loop controls for complex business workflows.

• LLM implementation:Integrate and optimize LLMs (Anthropic Claude, OpenAI GPT, Google Gemini, Llama, Mistral) via APIs

• RAG pipelines:Design retrieval-augmented generation solutions, including chunking, embeddings, hybrid search, re-ranking, and vector database management.

• Tool and integration layer:Implement function/tool calling and Model Context Protocol (MCP) servers to connect agents with enterprise systems, APIs, and data sources.

• Prompt and context engineering:Develop robust prompts, system instructions, structured outputs, and context-management strategies (including prompt caching) to maximize quality and cost efficiency.

• Evaluation and observability:Build evaluation frameworks (golden datasets, LLM-as-judge, regression testing) and monitor agents for accuracy, latency, cost, hallucination, and drift.

• Leadership:Mentor junior engineers, conduct code and design reviews, contribute to reusable accelerators, and support pre-sales technical discussions and PoCs.

• 5-8 years of experience in AI/ML or software engineering, with at least 1 or 2 years building LLM/GenAI applications in production.

• Strong Python proficiency experience with FastAPI/Flask, async programming, and clean, testable code.

• Hands-on experience with the Anthropic Claude API/SDK (messages, tool use, extended thinking, prompt caching, vision, batch processing) and at least one other LLM provider.

• Proven experience building AI agents using frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, Claude Agent SDK.

• Working knowledge of MCP, tool/function calling, and multi-agent orchestration patterns (planner-executor, supervisor, router, reflection).

• Solid understanding of embeddings, vector databases (Pinecone, FAISS, Chroma) and search relevance tuning.

• Experience with fine-tuning open-source models, and inference serving Ollama.

• Collaboration with data engineers, product owners, architects, and client teams.