Ontologies & Knowledge Graphs (Agentic AI, Surrogates, Physical AI)

AI Full Stack Engineer Ontologies & Knowledge Graphs (Agentic AI Surrogates Physical AI)

Experience: 6yrs

Job Location : Pune

Summary: Makes our simulation products AI-accessible. Our products have rich programmatic surfaces scripting APIs file formats data structures workflow logic that are powerful but not designed for AI consumption. This engineer defines the ontology schema that describes those surfaces builds the knowledge graphs on top of them and wraps them as structured typed interfaces that AI agents can discover and invoke.

Responsibilities:

• Define and maintain ontology schemas describing product capabilities entities and relationships
• Build and maintain knowledge graphs over product documentation APIs and simulation data
• Build structured tool interfaces exposing product capabilities to AI systems
• Write connectors to product APIs parsers and data access layers
• Implement retrieval and context layers over product knowledge
• Work with domain engineers to translate simulation workflows into discrete callable operations
• Review product ontologies for agentic-readiness across 23 products

Skills we need:

• Strong Python; experience building and consuming REST APIs
• Familiarity with graph databases and/or ontology/semantic modeling (RDF OWL property graphs or equivalent)
• Experience with at least one agent framework (LangChain LangGraph AutoGen CrewAI or similar)
• Understanding of how LLMs consume context and call tools
• Comfortable working within unfamiliar or undocumented codebases
• Systems thinker able to decompose a complex legacy workflow into discrete callable steps

Nice to have:

Vector databases; agent-tool interface development; parsing structured file formats; exposure to CAE/FEA/CFD surrogate modeling or physical AI.

Deliberately not required: Deep simulation domain knowledge domain engineers provide that. No PhD or ML research background.