AI Engineer, Ontologies, Knowledge Graphs

Cadence Design Systems

. Build ETL/ELT pipelines that extract data from source code, APIs, file formats, and documentation and load it into a structured knowledge store .

Posted 9/16/2026full-timeLivonia • Michigan • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Proficient in building ETL/ELT pipelines and developing data-access layers, with strong expertise in Python and REST API integration. Familiar with graph databases and semantic modeling, capable of collaborating with domain engineers to optimize complex workflows.

Highest-signal resume keywords

ETL/ELT Pipeline DevelopmentPython ProgrammingGraph Database FamiliarityREST API ExperienceData Modeling

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Applicant Tracking System Keywords

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Hard Skills

ETL/ELT Pipeline DevelopmentPython ProgrammingREST API DevelopmentGraph Database FamiliarityData ModelingMetadata ParsingKnowledge Graph ConstructionData Quality AssessmentAgent Framework ExperienceContextual Tool Utilization

Soft Skills

Systems ThinkingCollaboration

Tools & Technologies

LangChainLangGraphAutoGenCrewAIVector Databases

Industry Keywords

Computer ScienceMechanical EngineeringCAEFEACFD

Tech Stack

Tools & technologies

ETLPython

About the role

Key responsibilities & impact

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, including embeddings and 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
Build structured knowledge and interfaces over product capabilities across multiple products
Collaborate closely with domain engineers who provide subject-matter expertise

Requirements

What you’ll need

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
Nice to have: Vector databases
Nice to have: Data-access and API interface development
Nice to have: Parsing structured file formats
Nice to have: Surrogate modeling or related numerical methods
Deep or specialist domain expertise beyond working familiarity is not required
No PhD or ML research background required

Benefits

Comp & perks

Travel is not an expectation for this role
Occasional travel may occur for broad team alignment workshops, but these are infrequent