Lead Agentic AI Engineer, LLM & LangGraph
Overview
Build enterprise-grade AI agents that reason, plan, and execute autonomously. You'll architect scalable applications with LangGraph, RAG, and MCP, integrating cloud AI platforms like AWS Bedrock and Azure AI. You'll lead a team of engineers, shaping the technical roadmap from day one. This role demands deep expertise in LLM orchestration and production deployment.
What You'll Do8
- 1Architect and build multi-agent systems with LangGraph and LangChain, enabling complex task decomposition and execution.
- 2Design and implement RAG pipelines over enterprise data using Pinecone, Weaviate, or FAISS for context-aware retrieval.
- 3Integrate agent tools and APIs via MCP to extend capabilities autonomously.
- 4Deploy and scale AI agents on cloud platforms: AWS Bedrock, Azure AI, or Google Vertex AI.
- 5Lead a squad of 4-6 engineers, setting technical direction and code review standards.
- 6Ship the first production-ready agent within 90 days, handling real-time user queries with latency under 2 seconds.
- 7Implement observability and monitoring for agent behavior using LangSmith or OpenTelemetry.
- 8Drive evaluation and fine-tuning of LLMs using OpenAI, Anthropic, or open-source models.
Requirements8
- 15+ years building production AI/ML applications, with 2+ years focused on LLMs and agentic systems.
- 2Expert in LangGraph and LangChain for workflow orchestration and state management.
- 3Hands-on with RAG patterns, vector databases like Pinecone, and embedding models.
- 4Experience with MCP (Model Context Protocol) and tool integration.
- 5Proficiency in cloud AI services: AWS Bedrock or Azure AI.
- 6Strong coding in Python with asynchronous frameworks (FastAPI).
- 7Experience with Docker and Kubernetes for containerized deployment.
- 8Excellent communication to lead cross-functional teams and present to execs.
Salary Insight
Salary not disclosed in listing
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