Ontology Modeler

Job Title: Ontology  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.