AI Associate - MD

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.

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 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.

MD/DO  with active, unrestricted license.

3+ years clinical practice; 2+ years in quality, patient safety, or clinical operations.

Experience with clinical review of LLM outputs; building eval datasets; red-teaming/prompt testing; RAG assessment.

QI/patient safety expertise (PDSA, Lean/Six Sigma, RCA/RCA2, FMEA, CAPA).

Working knowledge of URAC, NCQA, CMS; familiarity with HEDIS/MIPS.

Proven track record of quality improvement within value-based care models.

Experience partnering with analytics/data science; interpret core performance metrics

Excellent documentation and stakeholder communication.

Experience building, deploying, and iterating, clinical AI agents

Proficiency in business intelligence and analytics. (e.g., Tableau/Power BI; basic SQL/statistics).

Experience in virtual care/care navigation and enterprise audits.