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
Location: Bengaluru, in office
Experience: 3–7 years
Notice period: immediate to 30 days preferred

📌 Knowledge Graph Engineer Rdf / Owl / Shacl Bengaluru 4–7 Years
🏢 AmagoPro Consulting Private
📍 Bengaluru