Scope:

• Design and develop multi-agent AI systems using LangGraph (ReAct agents, tool orchestration, memory)
• Own and optimize end-to-end RAG pipelines (embeddings, hybrid search, reranking, retrieval quality)
• Build and maintain LLM-powered data pipelines for code analysis, CRM comparison, and content synthesis
• Develop and refine prompt engineering strategies and evaluation frameworks for AI outputs
• Integrate LLM capabilities with graph data (Neo4j) and structured data sources
• Collaborate on architecture, code reviews, and AI best practices across the team
• Ensure scalability, performance, and cost optimization of AI systems in production

Skills:

• Strong experience in AI/LLM engineering and production systems
• Experience with LangChain / LangGraph (multi-agent systems)
• Solid knowledge of RAG, embeddings, vector search, and retrieval tuning
• Strong Python skills (FastAPI, asyncio)
• Experience with prompt engineering and LLM evaluation
• Hands-on with OpenAI / Azure OpenAI APIs
• Experience with PostgreSQL + pgvector
• Knowledge of API development and cloud (Azure)

Preferred:

• Neo4j / graph databases and Cypher
• Azure (AKS, Azure OpenAI)
• Hybrid search and reranking techniques
• AI observability (cost, performance, quality)
• CRM domain (Salesforce, Dynamics)

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