Remote Knowledge Graph Architect for AI in Construction

Eliassen Group; Dover (DE); Remote; USD 138,000 - 165,000; Full time

Job summary

Eliassen Group is seeking a Knowledge Graph Architect to lead the architecture and technical strategy for a construction-focused ontology, knowledge graph, and AI knowledge platform.

You will connect historical project and operational data across estimating, scheduling, project controls, field execution, equipment, cost, and lessons learned to create a reusable knowledge foundation that can support AI, GraphRAG, LLM, and agent-based applications.

Qualifications

• Proven track record in knowledge graph and ontology development.
• Experience with semantic modeling and graph databases.
• Ability to translate complex business data into scalable architectures.
• Effective cross-functional communication with technical and business teams.

Responsibilities

• Architect and develop an enterprise ontology and knowledge graph for construction and project data.
• Define relationships across projects, activities, estimates, schedules, costs, crews, equipment, and outcomes.
• Evaluate and incorporate standards including IFC, buildingSMART, LandInfra, and semantic web standards.
• Integrate enterprise data from multiple systems into the knowledge architecture.
• Design and guide GraphRAG, LazyGraphRAG, RAG, and AI agent architectures.
• Establish standards for ontology governance, provenance, and data quality.
• Partner with domain SMEs, enterprise architecture, data engineering, and AI teams.
• Design experiments to measure AI answer quality improvements over traditional RAG/LLM approaches.

Skills

Knowledge graph

Ontology design

Semantic modeling

Graph databases

RAG/LLM

Data integration

Communication

Job description

Eliassen Group is seeking a Knowledge Graph Architect to lead the architecture and technical strategy for a construction-focused ontology, knowledge graph, and AI knowledge platform.

You will connect historical project and operational data across estimating, scheduling, project controls, field execution, equipment, cost, and lessons learned to create a reusable knowledge foundation that can support AI, GraphRAG, LLM, and agent-based applications.