AI Content Optimization Engineer 5042

Title: AI Content Optimization Engineer (Automation Engineer) Location: Milwaukee, WI preferred / will consider 100% remote from anywhere in the United States Duration: 10+ months Perks: Benefits, free daily lunch when onsite Job Description: We’re seeking a hands‑on technical contributor to modernize HR content for AI‑powered employee experiences. This role focuses on transforming unstructured HR content into governed, AI‑ready knowledge assets that enable accurate, compliant retrieval at scale. You’ll partner with architecture, engineering, AI, legal, security, and HR teams to design content pipelines, retrieval architectures, and evaluation mechanisms that improve trust, accuracy, and self‑service outcomes. What You’ll Do • Design and implement pipelines to ingest, structure, and index HR content for AI retrieval. • Transform policies, SOPs, FAQs, and guides into structured or semi‑structured, AI‑ready formats. • Apply semantic chunking, embeddings, versioning, and indexing techniques. • Implement metadata standards, taxonomy, and content classification. • Translate governance and compliance requirements into enforceable system rules. • Enable privacy‑safe retrieval (RBAC, PII handling, audit logging). • Evaluate retrieval quality using metrics such as precision, recall, and answer faithfulness. • Instrument logging and observability to improve content health and retrieval accuracy. • Support pilots, launches, and scaling of AI‑enabled HR content experiences. Required Qualifications • Hands‑on experience delivering AI‑ready content or retrieval systems in an enterprise environment. • Strong knowledge Information architecture and content structuring • Metadata, taxonomy, and content classification • Retrieval‑augmented generation (RAG) or enterprise search • Experience with semantic chunking, embeddings, and indexing. • Familiarity with privacy‑safe AI patterns and governance controls. • Ability to operate as a lead contributor in cross‑functional teams. Nice to Have • Vector search, AI evaluation metrics, or content health monitoring. • Knowledge graphs or graph‑based retrieval (GraphRAG). • Search relevance tuning or pipeline instrumentation. • Cloud‑native platform integration experience. Must‑Have Focus Areas • AI‑Ready Content Engineering & Retrieval • Information Architecture & Metadata • Privacy‑Safe AI & Governance • Retrieval Quality & Observability