AI Engineer, Ontologies & Knowledge Graphs

We are looking for an engineer who builds the data and interface layer that makes complex software products programmatically understandable. Many powerful products expose rich but heterogeneous surfaces — source code, scripting APIs, file formats, data structures, and workflow logic. This role builds pipelines that extract and transform those surfaces into structured, queryable knowledge — designing the schemas, constructing the knowledge graphs, and exposing them through clean, typed programmatic interfaces for downstream consumption.

Job Responsibilities

• Build ETL/ELT pipelines that extract data from source code, APIs, file formats, and documentation and load it into a structured knowledge store.
• Design and maintain schemas and semantic data models capturing entities, relationships, and capabilities.
• Construct and maintain knowledge graphs over heterogeneous product data.
• Develop source and metadata parsers (including source-code/AST parsing) to extract structure automatically.
• Build typed programmatic interfaces and data-access layers over the knowledge layer.
• Implement retrieval and indexing layers (e.g., embeddings, RAG) over product knowledge.
• Work with domain engineers to decompose complex product workflows into discrete, callable operations.
• Assess data sources for coverage, quality, and schema completeness across multiple products.

Job Qualifications

• BS/MS in Computer Science, Mechanical Engineering, or similar.
• Strong Python; experience building and consuming REST APIs.
• Experience building data pipelines (ETL/ELT) over structured and unstructured data.
• Familiarity with graph databases and/or semantic/ontology 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 (retrieval, RAG, embeddings).
• Exposure to CAE/FEA/CFD or a related physical-simulation or engineering domain.
• Comfortable working within unfamiliar or undocumented codebases.
• Systems thinker — able to decompose a complex legacy workflow into discrete, callable steps.

Additional Skills/Preferences

Nice to have:

• Vector databases.
• Data-access and API interface development.
• Parsing structured file formats.
• Surrogate modeling or related numerical methods.

Deliberately not required:

• Deep or specialist domain expertise beyond working familiarity — domain engineers provide that.
• No PhD or ML research background required.

Additional Information

• Works across multiple products, building structured knowledge and interfaces over their capabilities.
• Collaborates closely with domain engineers who provide subject-matter expertise.
• Works with data pipelines, graph databases, and product API surfaces.
• Travel is not an expectation for this role. Occasional travel may occur for broad team alignment workshops, but these are infrequent.