Cadence Design Systems
Ontologies & Knowledge Graphs (Agentic AI, Surrogates, Physical AI)
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Builds ontologies, knowledge graphs, and structured tool interfaces that make Cadence simulation products accessible to AI agents. Responsibilities include modeling product capabilities, connecting APIs and data layers, implementing retrieval and context systems, translating simulation workflows into callable operations, and reviewing ontologies for agent readiness across multiple products.
At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology.
AI Full Stack Engineer, Ontologies & Knowledge Graphs (Agentic AI, Surrogates, Physical AI) —
Exp: 6+yrs
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 2–3 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.
We’re doing work that matters. Help us solve what others can’t.Cadence Design Systems Pune, Mahārāshtra, IND Office
Building No 1 , First Floor, Samrat Ashok Path, Commerzone IT Park, Yerawada, Pune, Maharashtra, India, 411006
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