Responsibilities
- Design, develop, and maintain formal ontologies using OWL, RDF/RDFS, and SPARQL aligned with DoD mission engineering requirements and semantic interoperability standards.
- Build and deploy knowledge graph solutions that integrate structured and unstructured data sources across heterogeneous, multi-domain defense systems.
- Apply and extend upper-level ontology frameworks, ensuring alignment with DoD and IC data standards.
- Design and implement graph-based AI and semantic solutions, including LLM-integrated pipelines, RAG architectures, and agentic workflows that leverage knowledge graph representations.
- Collaborate with engineers, systems architects, and mission-domain SMEs to translate operational requirements into actionable ontological models and knowledge architectures.
- Support semantic integration and data interoperability efforts across legacy and modern system architectures, including graph database deployments (e.g., Stardog, Neptune, Neo4j).
- Lead the development and writing of technical approaches for proposals related to supporting the Navy adopt AI technologies
Basic qualifications
- Bachelor's degree or higher in Computer Science, Information Science, Knowledge Engineering, or a related discipline; equivalent experience considered
- PhD with 2+ years of relevant experience, MA/MS with 5+ years of relevant experience, or BA/BS with 7+ years Hands-on experience with knowledge graph technologies including RDF, SPARQL, SHACL, and OWL for DoD or enterprise use cases Experience with schema design, ontology management, and knowledge graph curation Experience designing and developing end-to-end knowledge graph and AI data pipelines, including integration with LLMs or similar models Familiarity with graph database platforms such as Stardog, Blazegraph, Neo4j, or Amazon Neptune
- Hands-on experience with knowledge graph technologies including RDF, SPARQL, SHACL, and OWL for DoD or enterprise use cases
- Experience with schema design, ontology management, and knowledge graph curation
- Experience designing and developing end-to-end knowledge graph and AI data pipelines, including integration with LLMs or similar models
- Familiarity with graph database platforms such as Stardog, Blazegraph, Neo4j, or Amazon Neptune
- Ability to obtain and maintain a SECRET DoD Clearance
Preferred qualifications
- Experience with U.S. Navy Combat Systems
- 2+ years of hands-on Python experience, including frameworks such as TensorFlow, PyTorch, rdflib, or owlready2, and ETL pipeline tools (e.g., Apache NiFi, Airflow)
- Experience with full-stack web development, including REST API design and development (e.g., FastAPI, Flask, or Node.js), front-end frameworks (e.g., React or Angular), and containerization/deployment tooling (e.g., Docker, Kubernetes)
- Practical experience with NLP, semantic search, prompt engineering, and LLMs for enterprise-scale knowledge graph applications including RAG architectures.
- Experience with agentic AI systems and multi-modal model integration applied to knowledge engineering problems
- Demonstrated team leadership experience, with the ability to guide junior engineers and collaborate across engineering, research, and program management teams
- PhD in Computer Science, Knowledge Engineering, Mathematics, or a related field, or a publication record in semantic web and knowledge representation
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