Ontology Architect and Knowledge Modeling Specialist | SME

Amatriot Group, LLC; Chantilly (VA); Presencial; USD 129.000 - 205.000; Jornada completa

Descripción de la vacante

Amatriot Group, LLC seeks an Ontology Architect and Knowledge Modeling Specialist (SME) to design enterprise knowledge graphs and ontologies. You will translate business domains into semantic models, enabling interoperability, inference, and scalable metadata management across data platforms.

You will partner with data scientists, software engineers, and AI architects to integrate semantic frameworks with GraphDB and Neo4j, and to align governance workflows with enterprise data quality standards.

Formación

• Minimum of 10 years of experience in ontology/knowledge modeling.
• Strong background in semantic web standards OWL, RDF, SHACL.
• Experience with GraphDB or Neo4j and enterprise knowledge graphs.
• Proven ability to design domain ontologies and taxonomies for interoperability.
• Experience integrating semantic models with AI pipelines (RAG, vector search).
• Knowledge governance, metadata management, and data harmonization.

Responsabilidades

• Design and implement enterprise knowledge graphs and ontological models.
• Develop domain ontologies, taxonomies, and vocabularies with OWL/RDF/SHACL.
• Model complex relationships for semantic search and entity resolution.
• Collaborate with data scientists and engineers to integrate with GraphDB/Neo4j and AI pipelines.

Conocimientos

Semantic architecture

Knowledge graph design

Ontology development

Taxonomies & vocabularies

OWL, RDF, SHACL

RAG pipelines & vector search

Data governance & validation

Entity resolution & semantic search

Interoperability & metadata management

Herramientas

GraphDB

Neo4j

Descripción del empleo

Ontology Architect and Knowledge Modeling Specialist | SME

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.

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.

Qualifications

Education

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.