Senior Analyst

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