AI/ML Engineer (Contingent)

• Integrate AI Output into Operations: Integrate AI output into operations for use cases related to clinical decision support, risk prediction, operational analytics, and similar applications, using standard interfaces and reliability SLAs.

• Implement End-to-End Observability: Implement observability for ML pipelines covering data lineage, experiment tracking, model monitoring for quality, drift, and bias, and alerts routed to the platform's monitoring stack.

• Operate Trustworthy-AI Controls: Operate controls around privacy, safety, fairness, and transparency, maintaining risk assessments and audit evidence for high-impact use cases.

• Optimize Cost and Performance: Optimize cloud cost and performance for training and serving through autoscaling, job parallelism, caching, and partitioning, and report efficiency gains quarterly in an AI/ML Health Report.

• Operate Onboarding and Upgrade Processes: Stand up and operate AI/ML onboarding and upgrade processes that educate workgroups about the newest AI/ML tooling versions, and onboard users to AI/ML tools and features in alignment with platform provisioning and architecture patterns.

• Coordinate Across Workstreams: Coordinate with engineering and governance workstreams so AI/ML workloads leverage shared ingestion frameworks, catalog metadata and taxonomy, and common monitoring and alerting for repeatable, production-grade operations.

• Run Discovery and Pilots: Conduct discovery, prioritize intake, and run pilots for selected high-potential, early-stage AI concepts. Provide hands-on support in shared compute resources, including environment setup, model development assistance, and technical troubleshooting.

• Build Frameworks and Playbooks: Design frameworks, runbooks, and standardized KPIs to measure success and support future iterations, develop decision-readiness materials, and create a reusable pilot-design playbook for future innovation cycles.