AI/ML Engineer - Post-Deployment

• Hours / Pay Period: 80
• Benefits Eligible: Yes
• Remote worker: Flexibility of both remote and on-site work
• Compensation Detail: $116,043.20 - $168,292.80/ year. Education, experience and tenure may be considered along with internal equity when job offers are extended.
• Schedule Details: M-F 8am-5pm This is a hybrid position and must be located within 100 miles of a Mayo Clinic campus for occasional on-site expectations based on business needs.
• Weekend Schedule: NA
• Union Job: No
• Area of Interest 1: Information Technology
• Marketing Category 1: Information Technology
• International Assignment: No
• Overtime Rule: Salaried employee who does not receive overtime pay

AI/ML Engineers in AI Validation & Monitoring (AVM) apply data, systems, computer science, clinical workflow, and governance expertise to help ensure that post-deployment monitoring, reporting, and lifecycle evidence for clinical AI products is traceable, decision-ready, and supported by scalable governance technology. They work with AIA Governance Operations, AIA Governance Technologies, clinical and product teams, data and analytics partners, IT, architecture, patient safety, legal and regulatory functions, vendors, and other stakeholders to translate approved AIA Governance Policy, PDM and PDRS requirements, and evidence expectations into practical workflows, specifications, and review outcomes.

As the AI/ML Engineer - Post-Deployment Governance, with the functional assignment of PDRS TRex and Governance Technology Partnership, you will convert approved PDM and PDRS, evidence-lineage, reviewer, and policy requirements into functional requirements for TRex, dashboards, and related governance technology. You will support subject-matter review of PDM and PDRS assessment content when tooling, telemetry, data availability, evidence lineage, reporting workflow, or technology constraints affect governance adequacy; define workflows, evidence objects, required fields, decision states, business rules, traceability, and governance-acceptance criteria; and partner with AIA Governance Technologies on feasibility, backlog refinement, prototypes, user acceptance, release readiness, and defect impact.

• Eliciting and prioritizing requirements from approved policy, PDM and PDRS assessment reviews, recurring evidence and workflow gaps, Product Lead feedback, and post-deployment governance priorities; maintaining a traceable requirements backlog and prioritized roadmap recommendations.
• Translating governance policy and evidence needs into user stories, workflow specifications, data definitions, required fields, decision states, business rules, acceptance scenarios, and functional test cases.
• Defining reusable TRex content models and evidence objects that preserve policy-to-workflow and requirement traceability across metrics, sources, owners, cadence, product and model versions, limitations, actions, and handoffs.
• Developing dashboard and portfolio-reporting requirements that support PDRS status, evidence confidence, conditions, escalation, change and retesting, ownership, next actions, and decision-ready visibility.
• Reviewing PDM and PDRS content and supporting evidence prepared by AIA Governance Operations when tooling, telemetry, data availability, evidence lineage, or reporting workflow is material; documenting required corrections, limitations, and technology or evidence changes.
• Partnering with AIA Governance Technologies on technical feasibility, backlog refinement, prototypes, acceptance criteria, user-acceptance testing, release readiness, and assessment of defects or proposed changes against approved governance requirements.
• Performing governance acceptance of implemented workflows, evidence objects, dashboards, and reporting features; identifying policy-impacting defects, traceability gaps, and release conditions while preserving Governance Technology ownership of technical build and run.

• Converting recurring review findings into scalable TRex patterns, dashboard specifications, vendor expectations, enterprise data needs, templates, and technology priorities, and communicating requirements and findings clearly to technical and non-technical stakeholders.

  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.

Qualifications

• A master’s degree in engineering, computer science, mathematics, health science, or a related field and 1 year experience, or a bachelor’s degree with 3 years of experience.
• Experience applying AI and machine learning in production environments or similar highly regulated or technology focused industries, showcasing an understanding of healthcare technology.
• Skill in cloud infrastructure environment and software development tools.
• Experience working with large, complex, and heterogeneous data sets, preferably in healthcare.
• Skill in AI/ML techniques and frameworks.
• History of 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:
• A Ph.D. or other doctorate degree is preferred.
• 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 effectively to different project scenarios.
• 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.
• Experience with healthcare industry informatics standards, best practices, and common data models
• Demonstrated hands-on experience translating AI governance and requirements into TRex workflows, evidence objects, business rules, dashboards, user stories, acceptance criteria, functional tests, and release-readiness assessments.

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