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

This AI full-stack engineering role makes Cadence’s simulation products accessible to AI agents. The engineer will describe product capabilities through ontologies and knowledge graphs, then expose scripting APIs, file formats, data structures and workflows as structured, typed interfaces that agents can discover and invoke. The full-time position is located in Pune, India.

Responsibilities

• Define and maintain ontology schemas for product capabilities, entities and relationships; build knowledge graphs from product documentation, APIs and simulation data.
• Build structured tool interfaces and connectors to product APIs, parsers and data-access layers. Implement retrieval and context layers over product knowledge.
• Partner with domain engineers to break simulation workflows into discrete, callable operations, and review ontologies for agentic readiness across 2–3 products.

Requirements

• 6+ years of experience; strong Python skills and experience building and consuming REST APIs.
• Familiarity with graph databases or ontology and semantic modeling, such as RDF, OWL or property graphs. Experience with at least one agent framework, such as LangChain, LangGraph, AutoGen or CrewAI.
• Understanding of how LLMs use context and call tools; ability to work in unfamiliar or undocumented codebases and decompose complex legacy workflows into callable steps.

Preferred, not required: Experience with vector databases, agent-tool interfaces or parsing structured file formats; exposure to CAE, FEA, CFD, surrogate modeling or physical AI. Deep simulation-domain knowledge, a PhD and an ML research background are not required.