AI Knowledge Engineer

Required Qualifications • Bachelor's degree in Computer Science, Information Science, Data, or a related field — or equivalent hands-on experience • 3+ years hands-on with LLM applications: RAG pipelines, embeddings, vector search, and prompt/context engineering • Experience structuring unstructured content: taxonomies, ontologies, entity models, or metadata frameworks • Strong SQL; Python for pipeline and evaluation tooling • Proven ability to extract knowledge from non-technical SMEs and turn it into working artifacts quickly • Working familiarity with at least one AI application framework (LangChain, LlamaIndex, Semantic Kernel, or similar) Preferred Qualifications • Azure stack: AI Search, AI Foundry / Azure OpenAI, Fabric or Synapse, Cosmos DB • Agent patterns: function calling, MCP, multi-step orchestration • Evaluation tooling: RAGAS, DeepEval, LangSmith, Arize Phoenix, or similar • Knowledge graph exposure (property graphs, Neo4j) • Enterprise data governance or data catalog experience • Prior work in real estate, infrastructure, or asset-heavy industries Summary Builds the knowledge and context foundations that power AI agents and GPTs across business units. Translates BU domain expertise, data assets, and documentation into structured, governed knowledge that agents can reliably use. This is a delivery role: tight deadlines, multiple concurrent BU use-cases, and production-quality output. Responsibilities • Design and build knowledge bases, retrieval pipelines, and semantic layers backing agent and GPT deployments • Work directly with BU subject matter experts to capture domain knowledge, business rules, and terminology, and encode them into agent-usable form • Author and maintain agent context: system prompts, tool definitions, entity and attribute documentation, and grounding datasets • Build and run evaluation sets (golden evals) to validate agent accuracy against SME-verified answers • Curate and profile source data with SMEs to compress semantic authoring for new use-cases • Ensure all knowledge assets meet platform governance standards for lineage, access scope, and auditability