Graph Engineer

About the role

We're looking for a Graph Engineer to architect and build the knowledge graph that powers our platform’s understanding of complex construction projects. This isn't about plugging documents into a vector database—it's about designing a graph structure that captures the intricate relationships between tenders, specifications, drawings, RFQs, quotes, subcontractors, and regulatory requirements. You'll work at the intersection of knowledge representation and practical AI systems, turning terabytes of unstructured construction documentation into a queryable knowledge structure that enables our agents to reason about projects the way experienced estimators do.

Responsibilities

• Graph-first thinking: Design a knowledge graph from first principles in a complex, real-world domain (legal, medical, financial, engineering, or similar).
• Deep graph database experience: Production experience with Neo4j or comparable systems (Amazon Neptune, TigerGraph, JanusGraph). Think naturally about Cypher/Gremlin query optimization, index strategies, and memory management.
• Ontology design: Experience creating formal ontologies or taxonomies. Understand the tradeoffs between expressiveness and queryability, and know when to normalize vs. denormalize for performance.
• RAG foundations: Understand vector embeddings, semantic search, and retrieval-augmented generation. Understand where RAG falls short and why graph-based approaches matter for complex reasoning.
• Strong engineering fundamentals: Python fluency. Experience building data pipelines that process diverse document types at scale. Comfort with cloud infrastructure (AWS preferred).

Requirements

• Required: Graph-first thinking, deep graph database experience, ontology design, RAG foundations, strong engineering fundamentals.

Qualifications

• Preferred: Background in construction, engineering, or another domain with complex document interdependencies, familiarity with NLP/NER for entity extraction from unstructured text, experience with LLM-based information extraction and structured output, experience with graph neural networks or knowledge graph embeddings, contributions to open-source graph tools or published work on knowledge graphs.

Skills

• Graph-first thinking
• Deep graph database experience
• Ontology design
• RAG foundations
• Strong engineering fundamentals

Benefits

• Not specified

Pay

• Not specified

Schedule

• Not specified