AI Knowledge Graph Engineer at fast-growing GenAI startup

You will architect the foundational Knowledge Graph powering next-generation GenAI applications and internal LLM agents. By implementing GraphRAG, you will ensure AI responses are factually accurate, contextually rich, and traceable to verified sources. This hands-on role sits at the intersection of data engineering, semantic modeling, and prompt engineering within a high-growth environment.

Fast-growing Generative AI startup

• Architect core property graph schemas and maintain ontologies to represent complex business entities and hierarchies.
• Build robust ETL/ELT pipelines to ingest data from CRM and ERP systems into high-performance graph databases.
• Optimize retrieval strategies combining semantic vector search with structural graph traversals for LLM grounding.

• Has 3+ years of experience designing enterprise-scale Knowledge Graphs using Neo4j, Neptune, or TigerGraph.
• Possesses expert proficiency in Python for data manipulation, pipeline development, and AI application orchestration.
• Demonstrates deep understanding of RAG methodologies, vector databases, and semantic technologies like RDF or OWL.

• Lead the implementation of GraphRAG to solve hallucination challenges in enterprise-grade Generative AI.
• Work at a well-funded startup backed by top-tier VCs in the rapidly evolving LLM orchestration space.
• Direct impact on core product architecture, moving beyond simple vector search to complex, multi-hop semantic retrieval.