Description
Remote
Our client 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. This role requires expertise in designing and building enterprise knowledge graphs and ontologies applied to modern AI use cases.
Due to client requirements, applicants must be willing and able to work on a w2 basis. For our w2 consultants, we offer a great benefits package that includes Medical, Dental, and Vision benefits, 401k with company matching, and life insurance.
Rate: $100.00 to $120.00/hr. w2
JN -102026-109252
Responsibilities
Architect and develop an enterprise ontology and knowledge graph for complex construction and project data.
Define relationships across projects, activities, estimates, schedules, cost codes, crews, equipment, quantities, production rates, risks, changes, and outcomes.
Evaluate and incorporate relevant standards, including IFC, buildingSMART, LandInfra, and semantic web standards.
Integrate enterprise data from multiple operational and project systems into the knowledge architecture.
Design and guide GraphRAG, LazyGraphRAG, RAG, and AI agent architectures.
Show the full description
Establish standards for ontology governance, provenance, entity resolution, cross-system mappings, and knowledge quality.
Partner with domain SMEs, enterprise architecture, data engineering, AI engineering, and technology teams.
Design experiments to evaluate whether structured enterprise knowledge improves AI answer quality compared with traditional RAG and LLM approaches.
Experience Requirements
Strong experience in knowledge graph architecture, ontology development, semantic architecture, knowledge engineering, enterprise/data architecture, or AI/ML architecture.
Hands-on experience working with knowledge graphs, ontologies, semantic modeling, or graph databases.
Understanding of RAG/GraphRAG, LLMs, AI agents, and enterprise data integration.
Experience defining relationships and mappings across complex, disparate enterprise data sources.
Ability to translate complex business and operational concepts into scalable technical architectures.
Strong communication skills and ability to work across both technical teams and business/domain SMEs.
