Ontology & Semantic Modeler

Job Title: Ontology & Semantic Modeler

Job Overview

We are looking for an experienced Knowledge, Ontology & Semantic Modeler to design and develop enterprise ontologies, semantic models, and knowledge graphs. The ideal candidate will establish consistent business meaning across multiple data sources and build semantic foundations that enable AI-powered knowledge retrieval, reasoning, and enterprise intelligence.

The candidate should have strong expertise in ontology engineering, semantic data modeling, knowledge representation, and semantic technologies, with an understanding of modern AI architectures, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Graph RAG.

Key Responsibilities

• Design and develop enterprise ontologies, taxonomies, knowledge graphs, and semantic data models.
  
• Define business concepts, entities, relationships, hierarchies, rules, and business metrics.
  
• Develop semantic mappings between source systems, canonical data models, and business ontologies.
  
• Implement entity resolution, semantic enrichment, ontology alignment, and relationship discovery.
  
• Build enterprise semantic layers to support business queries, knowledge retrieval, and AI-driven applications.
  
• Apply knowledge representation, inference, and rule-based reasoning techniques.
  
• Establish semantic governance, data validation, lineage, and version management practices.
  
• Develop semantic queries and support natural language-to-structured query mapping.
  
• Collaborate with business stakeholders, data architects, data engineers, and AI teams to translate domain knowledge into reusable semantic models.
  
• Support AI applications through structured knowledge foundations, semantic retrieval, and knowledge graph integration.
  

Required Technical Skills

1. Ontology Engineering & Knowledge Graphs 

• Strong hands-on experience with RDF, RDFS, OWL, SKOS, SPARQL, JSON-LD, and SHACL.
  
• Expertise in ontology engineering, taxonomy design, and knowledge graph development.
  
• Experience with graph databases such as Neo4j, Amazon Neptune, or GraphDB.
  

2. Semantic Modeling & Reasoning

• Strong understanding of semantic data modeling, concept mapping, and ontology alignment.
  
• Experience in entity resolution, relationship modeling, and semantic enrichment.
  
• Knowledge of inference engines, knowledge representation, and rule-based reasoning.
  
• Experience defining business glossaries, semantic definitions, business metrics, and dimensional hierarchies.
  
• Ability to design semantic queries and map natural language queries to structured data queries.
  

3. Data Engineering & AI Technologies

• Good understanding of SQL, data modeling, metadata management, and data integration.
  
• Familiarity with enterprise semantic layers and semantic data architecture.
  
• Understanding of LLMs, RAG, Graph RAG, hybrid retrieval, and AI-powered knowledge retrieval.
  
• Python programming and experience with semantic web libraries are desirable.
  

Preferred Qualifications

• Hands-on experience building enterprise semantic layers and reusable knowledge models.
  
• Experience developing knowledge foundations for AI-powered enterprise applications.
  
• Exposure to integrating knowledge graphs with LLM-based applications and retrieval pipelines.
  
• Strong analytical, problem-solving, communication, and stakeholder collaboration skills.
  

Requirements

Architecture who have hands-on experience with semantic web standards and AI-enabled knowledge systems are encouraged to apply.