AI Knowledge Graph Engineer at fast-growing AI startup

Company Description

Fast-growing AI software startup

Job Description

You will design and build the foundational knowledge graph powering next-generation Generative AI applications and LLM-driven agents. By implementing GraphRAG strategies, you will ensure AI responses are factually accurate and traceable. This role sits at the intersection of data engineering and semantic modeling to bridge the gap between structured data and LLMs.

Location

Remote

Why this role is remarkable

• Lead the implementation of cutting-edge GraphRAG technology to solve factual hallucination in modern LLM applications.
• Join a well-funded startup backed by top-tier VCs at a pivotal stage of their platform's evolution.
• Work in a high-impact role where your architecture directly influences the accuracy and trust of customer-facing AI products.

What you will do

• Architect core conversational and reference graph schemas using property graph models to support complex multi-hop queries.
• Build robust ETL/ELT pipelines to ingest data from CRM and ERP systems while implementing entity resolution processes.
• Develop retrieval strategies combining semantic vector search with structural graph traversals using frameworks like LangChain or LlamaIndex.

The ideal candidate

• At least 3 years of experience designing and optimizing enterprise-scale knowledge graphs with Neo4j, Neptune, or TigerGraph.
• Expert proficiency in Python for data manipulation and building scalable data engineering pipelines.
• Strong understanding of RAG methodologies, vector databases, and grounding LLM responses through structural data context.