GenAI Lead Engineer

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

1) 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.

2) 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)

3) 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.

4) 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.