Job Title: Ontology & Semantic Data Modeler

Experience: 6–8+ Years

Location: Remote

Notice Period: Immediate Joiners Preferred

About the Role

We are looking for an experienced Knowledge Graph & Ontology Engineer to design, develop, and maintain enterprise ontologies, semantic models, taxonomies, and knowledge graphs. The ideal candidate should have robust expertise in ontology engineering, semantic data modeling, RDF, OWL, SPARQL, graph databases, and enterprise knowledge representation.

The role involves establishing consistent business meaning across multiple data sources, building reusable semantic models, and enabling AI-driven applications through semantic retrieval, knowledge representation, reasoning, and intelligent data integration. The candidate will collaborate with business and technical teams to translate complex domain knowledge into scalable, governed semantic solutions for enterprise intelligence and AI applications.

Key Responsibilities

- Design, develop, and maintain enterprise ontologies, taxonomies, knowledge graphs, and semantic data models.
- Define business concepts, entities, relationships, hierarchies, rules, and metrics to establish consistent semantic definitions.
- Develop semantic mappings between source data, canonical data models, and business ontologies.
- Perform entity resolution, semantic enrichment, ontology alignment, and relationship discovery across diverse data sources.
- Build semantic layers that support business queries, knowledge retrieval, and AI-enabled enterprise applications.
- Develop and maintain knowledge graphs using graph database technologies such as Neo4j, GraphDB,

or Amazon Neptune.
- Implement ontology standards and semantic technologies, including RDF, RDFS, OWL, SKOS, SPARQL,

JSON-LD, and SHACL.
- Design semantic queries and support the mapping of natural-language questions to structured queries.
- Implement knowledge representation, inference mechanisms,



and rule-based reasoning to derive meaningful insights.
- Establish semantic governance practices, including validation, metadata management, data lineage, and version control.
- Collaborate with business stakeholders, domain experts, data engineers, architects, and AI teams to convert domain requirements into reusable semantic models.
- Integrate semantic models and knowledge graphs with enterprise data platforms, LLM applications, and

Retrieval-Augmented Generation (RAG) solutions.
- Support Graph RAG, hybrid retrieval, and semantic search capabilities for enterprise AI applications.
- Ensure the accuracy, consistency, scalability, and reusability of semantic models across enterprise systems.

Must-Have Skills

- 6–8+ years of experience in ontology engineering, semantic modeling, knowledge graph development,

or enterprise data architecture.
- Strong hands-on experience in ontology engineering, taxonomy design, and semantic data modeling.
- Proficiency in semantic web standards and technologies such as RDF, RDFS, OWL, SKOS, SPARQL,

JSON-LD, and SHACL.
- Experience designing and developing knowledge graphs and modeling entities, relationships,

hierarchies, and business concepts.
- Hands-on experience with graph databases such as Neo4j, GraphDB, or Amazon Neptune.
- Strong understanding of ontology alignment, semantic mapping, entity resolution, and semantic enrichment.
- Experience with knowledge representation, inference, and rule-based reasoning.
- Ability to define business glossaries, semantic definitions, metrics, and dimensional hierarchies.
- Experience with semantic query design, SPARQL, and mapping natural-language queries to structured queries.
- Strong understanding of SQL, data modeling, metadata management, and data integration.
- Experience implementing semantic layers and reusable knowledge models for enterprise data and business intelligence.
- Familiarity with LLMs, RAG, Graph RAG, semantic retrieval, and hybrid retrieval architectures.
- Strong analytical, problem-solving, and stakeholder collaboration skills.

📌 Ontology & Semantic Data Modeler (India)
🏢 Evnek
📍 India