Ontology Architect and Knowledge Modeling Specialist | SME

The Amatriot Group; Chantilly (VA); On-site; USD 129,000 - 205,000; Full time

Job summary

The Amatriot Group seeks an Ontology Architect and Knowledge Modeling Specialist to design and implement enterprise knowledge graphs and ontological models that convert unstructured data into machine-readable intelligence. You will collaborate with data scientists, software engineers, and AI architects.

Responsibilities include building semantic architectures, enabling interoperability, governance, and data quality; integrating with GraphDB and Neo4j and supporting AI pipelines such as RAG and

Qualifications

• Bachelor’s degree required.
• Minimum of 10 years of experience.
• Expertise in semantic architectures and knowledge graphs.
• Experience with GraphDB and Neo4j.

Responsibilities

• Design and implement enterprise knowledge graphs and ontological models.
• Develop domain-specific ontologies and vocabularies.
• Model complex data relationships and business logic.
• Translate business domains into semantic models for semantic search and AI.
• Collaborate to integrate semantic frameworks with GraphDB and Neo4j.
• Establish governance and validation workflows.

Skills

Semantic architecture

Knowledge graphs

Ontology development

Graphs databases

Taxonomies

RAG pipelines

Data governance

Entity resolution

Semantic search

Education

Bachelor's degree

Tools

OWL

RDF

SHACL

GraphDB

Neo4j

Job description

Location: Chantilly, VA

Security Clearance: Active TS or higher [Required]

Job Type: Full-Time

Target Salary Range*: $129,000 - $205,000

• This represents the potential salary range for this position depending on education level, years of experience and/or certifications in addition to other position specific requirements which may impact salary

Position Overview

The Ontology Architect and Knowledge Modeling Specialist (SME) designs and implements enterprise knowledge graphs and ontological models that transform unstructured data into interconnected, machine-readable intelligence. This role develops semantic architectures supporting interoperability, inference, metadata management, and context-aware AI. Working with data scientists, software engineers, and AI architects, the specialist integrates semantic frameworks with enterprise graph databases and modern AI pipelines while establishing governance and validation practices to ensure data quality.

Key Responsibilities

Semantic Architecture and Knowledge Modeling

• Design and implement enterprise knowledge graphs and ontological models.
• Develop domain-specific ontologies, taxonomies, and controlled vocabularies using OWL, RDF, and SHACL.
• Model complex data relationships and business logic.
• Translate real-world business domains into structured semantic models supporting entity resolution, semantic search, and context-aware AI.

Interoperability and Metadata Management

• Design semantic schemas enabling cross-system interoperability and advanced inference.
• Support metadata management across diverse data repositories.

Graph Database and AI Integration

• Collaborate with data scientists, software engineers, and AI architects to integrate semantic frameworks with GraphDB and Neo4j.
• Integrate semantic models with AI pipelines, including retrieval-augmented generation (RAG) and vector search structures.

Data Governance and Quality

• Establish data governance practices and validation workflows.
• Develop data harmonization pipelines to eliminate information silos and enforce data quality.

Qualifications

Education

• Bachelor’s degree required.

Experience

• Minimum of 10 years of experience.

Skills

• Semantic architecture, enterprise knowledge graph design, and ontology development.
• Domain-specific taxonomies and controlled vocabularies.
• Semantic web standards, including OWL, RDF, and SHACL.
• Semantic schema design supporting interoperability, inference, and metadata management.
• Enterprise graph databases, including GraphDB and Neo4j.
• Integration of semantic frameworks with RAG pipelines and vector search structures.
• Data governance, validation workflows, and data harmonization.
• Semantic modeling supporting entity resolution, semantic search, and context-aware AI.