Associate AIML Engineer- Post Deployment Governance

Associate AI/ML Engineers in AI Validation & Monitoring (AVM) support the enterprise clinical AI governance review and consultation function for post-deployment monitoring reporting measurement and lifecycle evidence. Working under the guidance of more senior team members they collaborate with AIA Governance Operations product and operational teams clinicians data and platform partners vendors Governance Technology and other stakeholders to organize evidence apply approved methods identify completeness and traceability gaps and prepare decision-ready review materials that support safe effective and accountable use of AI in clinical and operational settings.

As the Associate AI/ML Engineer - Post-Deployment Governance serving in the functional assignment of Monitoring Reporting and Lifecycle Enablement you will prepare product-level evidence maps connecting required signals to source systems owners cadence product and model versions limitations Post-Deployment Monitoring (PDM) and Post-Deployment Report Summary (PDRS) domains actions and handoffs. You will support monitoring and reporting readiness across full implementation recurring reporting post-deployment changes and legacy products; collect and structure evidence using approved templates checklists and readiness methods; and prepare draft AVM comments review findings consultation materials and escalation packages.

• Preparing and maintaining product evidence and lineage maps that connect monitoring signals metrics source systems owners cadence collection methods product and model versions limitations PDM and PDRS domains actions and reporting handoffs.
• Supporting monitoring and reporting readiness reviews for full implementation recurring PDRS post-deployment changes and legacy products by applying approved templates checklists rubrics evidence requirements and readiness criteria.
• Performing completeness and traceability checks for sources owners cadence versions limitations baselines targets thresholds conditions actions and transition information; identifying missing inconsistent or unsupported evidence for assigned reviewers.
• Reviewing PDM and PDRS content and drafting factual corrections clarification questions evidence-gap and limitation summaries standard AVM comments and monitoring-readiness findings.
• Organizing PDRS review packages evidence references owner and source matrices action and condition trackers follow-up records ownership verification lifecycle handoff materials and transition plans.
• Supporting post-deployment change and legacy-product reviews by tracing version metric baseline threshold monitoring-continuity reporting-cadence evidence-gap and interim-control impacts for Engineer or Principal review.

• Coordinating evidence and status information with Product Leads product and operational owners data and platform partners vendors and other stakeholders; escalating substantive interpretation unresolved risk or complex PDM and PDRS questions.

  This vacancy is not eligible for sponsorship/ we will not sponsor or transfer visas for this position. Also Mayo Clinic DOES NOT participate in the F-1 STEM OPT extension program.
  
  

• A bachelors degree in engineering computer science health science or a related field
• Knowledge in applying AI and machine learning in production environments showcasing an understanding of healthcare technology.
• Knowledge in cloud infrastructure environment and software development tools.
• Skill in AI/ML techniques and frameworks.
• Skill in collaborating across diverse teams and effectively communicating complex technical concepts to non-technical stakeholders.
• Familiarity with best practices in data engineering data science AI Engineering and the MLOps communities.
• Strong interpersonal communication and time management skills.
  

Preferred Qualifications:

• Knowledge of the healthcare domain including clinical workflows electronic health records medical terminologies regulatory requirements and industry standards.
• Familiarity with systems or quality engineering best practices regulatory standards and compliance frameworks with the ability to adapt these effectively to different project scenarios.
• Experience in user-centered design human factors engineering usability testing methodologies and evaluation across AI product development. Ability to conduct expert reviews using established usability practices and methods. Presents findings in easy-to-understand terms for the business or clinical practice.
• Ability to articulate complex technical concepts to diverse audiences facilitating clear understanding and engagement from technical and non-technical stakeholders.
• Ability to manage a varied workload of projects with multiple priorities and stay current on healthcare trends.
• Demonstrated hands-on experience using the TRex assessment application to build evidence maps verify post-deployment monitoring readiness ownership cadence versions limitations and lifecycle handoffs for AI tools deployed in Epic ANIMATE and comparable clinical environments.