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Senior Engineer – Ontology & Knowledge Graph solutions

Job ID
499147
Posted since
24-Mar-2026
Field of work
Research & Development
Company
Siemens Healthcare Private Limited
Experience level
Experienced Professional
Job type
Full-time
Work mode
Office/Site only
Employment type
Permanent
Location(s)
  • Bangalore - Karnataka - India
We are seeking a highly skilled on Ontology Expert & Knowledge Graph with expertise in ontology development and knowledge graph implementation. This role will be pivotal in shaping our data infrastructure and ensuring accurate representation and integration of complex data sets. You will leverage industry best practices to design, develop, and maintain ontologies, semantic and syntactic data models, and knowledge graphs that drive data-driven decision-making and innovation within the company.

Job Purpose: 
The role of Ontology & Knowledge Graph / Data Engineer is to design, develop, implement, and maintain enterprise ontologies in support of Organizations Data Driven Digitalization strategy.
This role combines architecture ownership with hands-on engineering: you will model ontologies, stand up graph infrastructure, build semantic pipelines, and expose graph services that power search, recommendations, analytics, and GenAI solutions for our organization.
Seeking highly skilled motivated expertise to drive the development and shape the future of enterprise AI by designing and implementing large-scale ontologies and knowledge graph solutions. You’ll work closely with internal engineering and AI teams to build scalable data models that enable advanced reasoning, semantic search, and agentic AI workflows.

Key Responsibilities:    

1. RDF/OWL Ontology Engineering
• Design and maintain enterprise ontologies using RDF, RDFS, OWL 2, SKOS, SHACL following formal ontology design patterns.
• Capture and formalize domain knowledge into logically consistent ontological structures, reusing global standards whenever possible (e.g., schema.org, SNOMED, FHIR RDF if healthcare, ISO/IEC vocabularies).
• Define modelling guidelines: 
• identity management (IRIs, URI base strategies)
• class/property axioms
• equivalence & alignment rules
• disjointness, domain/range semantics
• open world and monotonic reasoning principles
• Implement ontology governance, versioning, namespace strategies, modularization patterns, and change management processes.
 
2. RDF Knowledge Graph Implementation
• Build and maintain W3C compliant knowledge graphs using triple stores such as GraphDB, RDF4J, Stardog, Blazegraph, RDFox, or Apache Jena based systems.
• Design RDF data models aligned with enterprise ontologies.
• Develop semantic ingestion pipelines using: 
• RML, R2RML
• SPARQL CONSTRUCT transformations
• custom Python based RDF generation
• Optimize SPARQL queries for reasoning enhanced graph stores.
• Implement inferencing strategies (RDFS/OWL profiles) appropriate for data validation, classification, or semantic enrichment.
 
3. SHACL-Based Data Quality & Semantic Governance
• Build SHACL Shapes for structural and semantic validation of data.
• Define constraint vocabularies to enforce modelling policies (cardinalities, value ranges, qualified constraints, logical shapes).
• Integrate validation pipelines into ETL/ELT workflows and CI/CD.
• Establish semantic governance processes: 
• modelling reviews
• ontology approval workflows
• vocabulary stewardship
• controlled evolution of semantic assets
• Ensure RDF graph quality, consistency, and interoperability across systems and data domains.
 
4. Integration with Enterprise Architecture and AI Systems
• Enable semantic search, reasoning-enhanced analytics, and hybrid neuro symbolic approaches.
• Provide semantic grounding for GenAI systems, including: 
• RAG indexing strategies aligned with ontology IRIs
• semantic retrieval using SPARQL and embedding combinations
• orchestration of agentic workflows with ontological constraints
• Collaborate with data engineering and software engineering teams to integrate semantic layers into enterprise platforms, metadata repositories, APIs, and digital threads.
 
5. Research, Methodology & Innovation
• Stay current with advances in: 
• ontology engineering methodologies (e.g., OntoClean, NeOn, DOLCE patterns)
• new W3C recommendations
• SHACL extensions and reasoning frameworks
• LLM–symbolic hybrid systems
• Prototype innovative methodologies for enterprise semantic modelling and semantic AI.
• Advise on semantic KPIs, ontology maturity, and modelling strategy.
 
Experience:
• 4–6 years of industrial experience in AI [OR] Data Science [OR] Data Engineering.
• 2–3 years of hands-on experience building ontologies and knowledge systems.
• Experience building and managing RDF knowledge graphs, not property graphs.
• Strong experience with at least one enterprise triple store (GraphDB, Cambridge Semantics, Stardog, RDFox, etc.).
• Familiarity with Gen AI concepts including retrieval-augmented generation and agent-based AI.
 
Mandatory Semantic Expertise
• RDF, RDFS, OWL 2, SHACL (Core + Advanced), SKOS
• SPARQL 1.1 (queries, updates, federated queries, reasoning-aware querying)
• Ontology design principles, modularization patterns, equivalence/alignment strategies

Semantic Engineering & Tooling
• Triple stores / RDF databases
• Mapping tools (RMLMapper, Ontop, SPARQL-based ETL)
• Python for RDF processing (RDFLib, SPARQLWrapper, pySHACL)

Complementary Skills
• Experience with GenAI frameworks (LangChain, LangGraph) for semantic coordination
• Familiarity with cloud infrastructures (AWS, Azure, GCP)