Location: Remote / Hybrid Type: Full-time / Freelance (negotiable) About Us & Our Vision: We are revolutionizing how large enterprise companies understand their mission-critical ERP systems and complex, domain-specific business rules. Instead of relying on generic LLM guesses and hallucinations, we are building a high-precision, domain-specific Knowledge Graph queried by intelligent, specialized AI agents. Your Role: In the initial phase of our product, we work with massive, static data extracts (JSON/XML dumps of enterprise codebases, configuration tables, and complex regulatory manuals). Your job is to transform this data chaos into a structured, highly interconnected, and queryable Knowledge Graph (Neo4j) and to set up a robust RAG pipeline for our AI agents. Your Core Responsibilities: Data Parsing: Develop robust Python pipelines to ingest, clean, and structure complex, nested JSON/XML data extracts from legacy enterprise systems. Graph Engineering: Design the data model and build a Neo4j Knowledge Graph (Cypher) that maps the relationships between system components, database schemas, and business configurations. Mock-API Development: Build a Python service (FastAPI) that provides data to our LLM agents via the Model Context Protocol (MCP) – simulating a live connection to a real enterprise backend. Vector Search / RAG: Chunk and embed domain-specific rulebooks and documents (PDFs/XMLs) into a vector database (e.g., AlloyDB pgvector or Pinecone). Your Profile (Must-Haves): Strong Python skills for complex data manipulation and backend development. Deep, practical knowledge of Neo4j and the Cypher query language. Experience in Data Engineering (ETL pipelines, handling large JSON/XML files). Solid understanding of RAG architectures (Retrieval-Augmented Generation) and Vector Databases. Analytical mindset: You can quickly grasp highly complex data structures and model them into efficient graphs. Bonus (Nice-to-Have): Basic understanding of general ERP systems (relational databases, foreign keys, system configurations, object-oriented code structures). Experience with the Model Context Protocol (MCP). What We Offer: Greenfield Project: No legacy systems to maintain. You make the technological decisions from Day 1. State-of-the-Art AI: Work at the absolute bleeding edge of current AI development (Multi-Agent Systems, 1M+ Token Contexts, GraphRAG). Flexibility: 100% remote work and flexible hours, focused on output and results, not presence. Impact: Build a product that solves a massive, expensive pain point in critical enterprise infrastructure.