GenAI Lead Engineer

Job description

**Role Overview** We are looking for a **GenAI/LLM Engineer** with strong **Python engineering** skills and proven experience building **production-grade Retrieval-Augmented Generation (RAG)** systems using **LlamaIndex and/or LangChain** , and integrating **vector databases (Pinecone preferred)** . The role will focus on designing scalable RAG pipelines, implementing advanced retrieval strategies, and building **MCP/tool-calling connectors** to expose enterprise APIs as agent tools for read/write operations. **Key Responsibilities** - RAG Pipeline Design & Development - Design and develop end-to-end RAG pipelines, including: - Data ingestion - Document parsing and preprocessing - Chunking strategies - Embedding generation - Indexing into Pinecone (preferred) - Retrieval and response generation - Build production-ready semantic retrieval solutions and continuously improve relevance/grounding quality. - Implement and optimize advanced retrieval strategies, including semantic search and retrieval tuning. - Agent Tooling & MCP Integrations - Build and integrate MCP connectors to expose internal/external system APIs as agent-callable tools (read/write). - Contribute to agent orchestration patterns including: - Intent routing (e.g., deciding between RAG vs MCP vs workflow) - Tool selection and execution sequencing - Agent reliability patterns (fallbacks, retries, observability) - Security, Reliability & Performance - Apply security controls and handle authentication/authorization tokens, ensuring safe access to enterprise systems. - Optimize AI/ML workflows for performance, scalability, and reliability (latency, throughput, cost, robustness). - Ensure seamless deployment and integration across environments in collaboration with platform/DevOps teams. - Cross-functional Collaboration - Work closely with product, backend, data engineering, and platform teams to ensure successful integration and delivery. - Contribute to design discussions, technical documentation, and best practices for GenAI application engineering. **Required Skills & Qualifications** - 5?8 years of experience in software engineering and/or data engineering. - 2+ years of hands-on experience building LLM/GenAI applications. - Strong programming expertise in Python. - Proven production experience with LlamaIndex and/or LangChain, especially for RAG systems. - Hands-on experience with vector databases; Pinecone preferred. - Strong understanding of retrieval concepts, embeddings, indexing, and semantic search. **Preferred / Good-to-Have** - Knowledge of MCP/tool-calling patterns; FASTMCP experience is a strong plus. - Experience with agent frameworks, tool routing, and workflow orchestration. - Familiarity with observability for GenAI apps (logging, tracing, evaluation, prompt/versioning). **What Success Looks Like (KPIs/Outcomes)** - High-quality RAG pipeline delivering accurate, grounded responses with measurable improvements in relevance. - Reliable MCP connectors enabling safe tool-based automation across enterprise systems. - Reduced latency and improved scalability with robust security and token management. - Smooth integration and deployment through strong collaboration and engineering discipline. Microsoft Dynamics