Ontology Specialist - Data & AI

Role Overview

• We are seeking an experienced
• Ontology Specialist to build the semantic foundation of an enterprise Data & AI ecosystem. The role involves designing and implementing enterprise ontologies, knowledge graphs, and reusable semantic models to connect fragmented data across product engineering, manufacturing, supply chain, operations, and commercial functions.
• The specialist will collaborate with business stakeholders, enterprise architects, data engineering teams, and AI professionals to develop contextual, governed, and AI-ready data assets that enable advanced analytics, intelligent decision-making, digital twins, recommendation systems, and enterprise AI solutions.

Key Responsibilities

• Design, develop, and implement enterprise ontologies and knowledge graphs across product engineering, manufacturing, supply chain, and operations domains.
• Define graph schemas, taxonomies, controlled vocabularies, and semantic models aligned with enterprise lakehouse platforms, Power BI, AI/ML pipelines, and business applications.
• Integrate structured, semi-structured, and unstructured data using Python, Spark/PySpark, SQL, and data orchestration pipelines.
• Implement entity resolution, relationship extraction, semantic enrichment, and knowledge graph construction.
• Develop and optimize graph queries and analytics using SPARQL, Cypher, Gremlin, and KQL graph semantics.
• Optimize graph performance through effective indexing, partitioning, caching, and query tuning.
• Integrate knowledge graphs with Azure OpenAI, Microsoft Copilot, and agentic AI frameworks to enable GraphRAG, hybrid retrieval, semantic grounding, and explainable AI outcomes.
• Establish ontology lifecycle governance, including semantic consistency, metadata management, data lineage, stewardship, versioning, and change control.
• Collaborate with domain experts, business stakeholders, data architects, and AI teams to translate business requirements into scalable semantic solutions.
• Serve as a technical subject matter expert for ontology engineering, knowledge graphs, and enterprise semantic architecture initiatives.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Information Systems, Engineering, or a related discipline.
• 8+ years of experience in data engineering, data architecture, or related technical roles.
• At least 3 years of hands-on experience in enterprise ontology engineering, knowledge graphs, or semantic architecture.
• Strong expertise in semantic web technologies, including RDF/RDFS, OWL, SPARQL, SHACL, and SKOS.
• Experience with ontology modeling tools such as Protégé or equivalent platforms.
• Hands-on experience with Neo4j or similar graph database platforms.
• Proficiency in graph query languages, including Cypher, Gremlin, and SPARQL, along with graph performance optimization techniques.
• Practical experience with enterprise lakehouse/data lake architectures and Azure data services or Microsoft Fabric.
• Strong programming and data processing skills in Python, PySpark, and SQL.
• Experience implementing GraphRAG, vector databases, hybrid retrieval, and knowledge graph integration with Azure OpenAI or other LLM-based platforms.
• Strong analytical, problem-solving, communication, and stakeholder collaboration skills.

Preferred Qualifications

• Experience with Stardog, Ontotext, or equivalent ontology and knowledge graph platforms.
• Familiarity with Data Mesh architectures and enterprise semantic-layer implementations.
• Experience working with manufacturing, automotive, aerospace, industrial products, or engineering data.
• Understanding of digital twins, AI-driven analytics, and enterprise knowledge management.
• Experience delivering scalable semantic solutions across complex enterprise data ecosystems.

Key Technical Skills

Ontology & Semantic Modeling:

RDF/RDFS, OWL, SHACL, SKOS, Protégé

Knowledge Graphs:

Neo4j, Stardog, Ontotext, Graph Schema Design

Query Languages:

SPARQL, Cypher, Gremlin, KQL Graph Semantics

Programming & Data Engineering:

Python, PySpark, Spark, SQL

Cloud & Data Platforms:

Microsoft Azure, Microsoft Fabric, Lakehouse Architecture

• AI & LLM Integration:
• Azure OpenAI, Microsoft Copilot, GraphRAG, Agentic AI, Vector Databases, Hybrid Retrieval
• Governance:
• Metadata Management, Data Lineage, Semantic Governance, Versioning, Data Stewardship