AI Governance Engineer

The AI Governance Engineer ensures the safe, effective, and ethical deployment of AI/ML across the FHSC system. This role operationalizes the governance framework for the full AI lifecycle, conducts technical due diligence of internal and external AI systems, and establishes robust post-implementation monitoring and auditing to safeguard patient care, regulatory compliance, and institutional integrity. The engineer partners closely with clinical leaders, data scientists, IT, Legal, Compliance, Safety/Risk, and Procurement to embed responsible AI practices in both vendor and in-house solutions. Essential Functions: Evaluation and validation of external/vendor AI tools. Lead pre-implementation risk assessments for commercial AI/ML tools used in clinical, research, and administrative contexts. Assess vendor documentation for alignment with FHSC governance standards, ethical principles, and regulatory requirements (e.g., FDA SaMD guidance and GMLP, HIPAA, ONC regulations, NIST AI RMF, emerging AI/LLM standards). Validate vendor claims and performance: generalizability to FHSC populations, calibration, bias/fairness across subgroups, robustness, transparency, and explain ability within FHSC data and workflows. Evaluate LLM-specific risks (e.g., hallucination rates, prompt injection/jailbreaks, data leakage/PII exposure, harmful/biased outputs) and verify mitigations. Internal AI architecture and framework design. Assess existing AI/ML technical architectures, data pipelines, and MLOps workflows used by clinical and research teams; identify gaps and remediation plans. Co-develop and maintain SOPs covering the full AI lifecycle: data sourcing and quality, labeling, privacy and de-identification, feature engineering, model development, validation, documentation, versioning, deployment, change management, and retirement. Define and promote Responsible AI practices: bias detection/mitigation, explainability, human-in-the-loop oversight, safety testing, and reproducibility. Advise teams on privacy-by-design, security-by-design, and safety-by-design, including PHI minimization, access controls, and differential privacy where appropriate. Ongoing monitoring and algorithm vigilance Design, implement, and manage a continuous monitoring framework (algorithm vigilance) for all deployed AI/ML systems. Define and track KPIs and risk indicators: accuracy, calibration, sensitivity/specificity, AUROC/PR, subgroup performance/fairness, model/data drift, latency, uptime, hallucination/toxicity rates (for LLMs), override rates, and clinical/operational impact. Build automated dashboards and alerting for real-time issue detection; implement thresholds, canary releases, rollback/kill-switch and fallback procedures. Coordinate with Safety and Risk on incident triage, root-cause analysis, corrective action plans, and post-incident reviews; run periodic tabletop exercises. Policy, governance, and reporting Contribute to FHSC’s AI Governance Policy, Risk Management Framework, and associated standards, aligning with NIST AI RMF, ISO/IEC 23894, FDA/IMDRF SaMD, HIPAA, and emerging regulations. Prepare and present risk assessments, validation reports, and operational metrics to the AI Governance Committee, executive leadership, and clinical/operational stakeholders.

QUALIFICATIONS: Bachelor's Degree Computer Science, Data Science, Engineering, or other related field. 3–6 years of experience in AI/ML model validation, AI observability/evaluations, technical risk management, or governance in a regulated environment. Technical Knowledge, Skills, and Abilities: Strong understanding of ML fundamentals and MLOps/model lifecycle management, statistical evaluation, data governance, and LLM evaluation and risk mitigation (e.g., benchmarking, robustness, bias/toxicity, hallucination). Familiarity with regulatory frameworks relevant to health AI: FDA SaMD and GMLP, HIPAA, ONC/HTI-1; and industry frameworks such as NIST AI RMF (and awareness of emerging rules). Demonstrated ability to translate complex technical findings about performance, bias, and architecture into clear, actionable insights for non-technical stakeholders. Proficiency with Python and SQL, and with tools for experimentation, versioning, and monitoring (e.g., MLflow/Weights & Biases, Evidently/WhyLabs/Fiddler/Arize, Great Expectations, Git).