• Lead pre-deployment validation • Produce model cards/transparency (intended use, limits, monitoring, rollback) • Partner on data quality (provenance, representativeness, refresh cadence, privacy) • Build LLM evaluation datasets/gold standards; run clinical red-teams and prompt testing; refine prompts/guardrails; assess RAG fidelity • Train clinicians to oversee AI tools and adjudicate exceptions; create SOPs/checklists/escalations for LLM use. • Collaborate with engineering and product, security, compliance, legal, care and service line leaders • Stand up/administer clinical AI governance (ethics, safety, regulatory, privacy, change control, documentation) in partnership with the Patient Safety Officer • Define clinical use criteria and human-in-the-loop guardrails • Maintain governance artifacts (charter, SOPs, decision logs) and audit readiness. • Support clinical service-line QA; URAC/NCQA activities; policy/standards/rubrics management. • Lead/participate in event detection, RCA/FMEA, CAPA; education (CME/CE, targeted training). • Support client/payer audits, RFPs, VBC metrics (HEDIS, MIPS), implementations, and escalations, grievances/appeals. • Lead and/or support cross-functional efforts to analyze clinical quality performance data, identify improvement opportunities, and translate value-based care insights into health plan design enhancements that optimize outcomes, member experience, and cost efficiency. • Conduct ongoing assessments of clinical services, applying continuous improvement and evidence-based methodologies to ensure service effectiveness, measurable patient outcomes, and alignment with organizational quality standards.