AI Knowledge Engineer
Core
Design and build semantic knowledge systems, knowledge graphs, and governance frameworks to enable AI agents and retrieval systems to reason over complex enterprise information.
Role type
AI Knowledge Engineer
Builds
Semantic knowledge models, knowledge graphs, taxonomies, ontologies, and metadata standards for enterprise AI
Domain
Cybersecurity / Enterprise Information Management / Generative AI
Deliverable
production ML models | infrastructure
Required skills
Python, Large Language Models (LLMs), semantic modeling, knowledge representation, data governance, ontology development, taxonomy management, metadata design, knowledge graphs, graph databases, retrieval-augmented generation (RAG), agentic AI systems, AI evaluation methodologies
Preferred skills
LangGraph, LangChain, LlamaIndex, MCP, cybersecurity concepts
Technologies
Python, LLMs, knowledge graphs, graph databases, LangGraph, LangChain, LlamaIndex, MCP
Responsibilities
Design and maintain semantic models representing business concepts and organizational knowledge; Develop and manage knowledge representations for AI reasoning; Build and enhance knowledge graphs to connect disparate systems; Create and maintain taxonomies, ontologies, and governance frameworks; Design knowledge ingestion workflows for AI-consumable assets; Collaborate with AI teams to integrate knowledge systems with LLM applications; Develop evaluation frameworks for AI agent effectiveness; Design approaches for governing federated knowledge repositories; Identify knowledge flows across systems and teams; Develop mechanisms for factual grounding and explainability of AI outputs; Analyze organizational knowledge assets for automation opportunities; Work with stakeholders to develop scalable knowledge solutions; Evaluate emerging AI and semantic technologies
Seniority
Early-career, hands-on IC