About the role
Our client is building an enterprise knowledge graph that connects engineering data across vehicle models, components, software, tests and suppliers, and makes it usable by AI assistants and agents. You’ll design the semantic model, build the pipelines that feed it, and connect it to LLMs. What you’ll do
• Design and maintain ontologies in OWL/RDF, and data validation rules in SHACL
• Build ETL pipelines that bring data from relational databases, APIs and documents into the knowledge graph
• Write and optimise SPARQL queries
• Work hands-on with graph platforms such as Ontotext GraphDB, Apache Jena Fuseki, Stardog, Amazon Neptune or Neo4j
• Combine the knowledge graph with LLMs: GraphRAG, text-to-SPARQL, and AI agents that automate tasks
• Work with engineers and domain experts to turn real engineering knowledge into a clean, reusable model Must have




• Hands-on project experience with RDF, OWL and SHACL, not just coursework
• Experience with at least one graph database or triple store
• Working knowledge of SPARQL
• Python for data processing and automation
• Experience building data pipelines into a graph Valuable to have
• GraphRAG, LLM or AI agent projects that use a knowledge graph
• Tools such as Protégé, TopBraid, RDFLib, pySHACL, RML/R2RML or Ontop
• Reuse of standard vocabularies (SKOS, schema.org, PROV-O and similar)
• Experience in automotive, manufacturing, engineering, life sciences or publishing data Details
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Location:
Bengaluru, in office
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Experience:
3–7 years
• Notice period: immediate to 30 days preferred

📌 Knowledge Graph Engineer (Bengaluru)
🏢 AmagoPro Consulting Private
📍 Bengaluru