Mid-Level AI Engineer

Job Description

Insight Global is seeking a Knowledge Engineer to assist with a Data Center client's needs within their AI org. This resource will build the knowledge and context foundations that power AI agents and GPTs across client 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.

• 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.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment irrespective of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances.

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• 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 (Lang Chain, Llama Index, Semantic Kernel, or similar)
• Azure stack: AI Search, AI Foundry / Azure OpenAI, Fabric or Synapse, Cosmos DB
• Agent patterns: function calling, MCP, multi‑step orchestration
• Evaluation tooling: RAGAS, Deep Eval, Lang Smith, 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