AI Vendor Governance Manager

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πŸ“ United States
πŸ“… Posted 2026-08-08 Β· via Himalayas
🏷 AI-Governance,Vendor-Risk-Management,Third-Party-Risk-Management,IT-Risk-Management,Compliance,AI-Governance-Architect,AI-Governance-Executive,AI-Governance-Consultant,AI-Policy-Manager
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Location: Atlanta, GA / Remote
Department: Legal

Reports to: Assistant General Counsel – Privacy, AI, and Data Governance
Why Join Omnicell ?

Omnicell is building its AI governance program and we are looking for a Manager, AI Vendor Governance to join our Legal Department and own how AI enters the company through third parties.

As a foundational member of Omnicell ’s AI Governance Program, you will design and operate a third-party intake process that identifies AI solutions entering the organization, evaluates them against the company’s responsible-AI and risk standards, and determines the level of risk each presents to the business. Working closely with Procurement, Legal, Privacy, Security, Quality, and business owners, you will establish questionnaires, risk-tiering criteria, and review workflows that allow Omnicell to adopt AI tools quickly and responsibly.
What You’ll Do (Key Responsibilities)

As a Manager, AI Vendor Governance, you will:

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Primary Impact - Give Omnicell a clear, defensible view of every AI solution entering the business through a third party, so that teams can adopt AI tools quickly and with confidence, and so that customers, regulators, and patients can trust how those tools are vetted and governed.

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Program build-out – Build, implement, and mature Omnicell ’s AI vendor governance program, including its policies, standards, intake process, and review workflows

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Third-party AI intake – Design and operate a third-party intake process that identifies AI solutions entering the organization, including AI embedded within broader products and services, and routes them for risk review

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Risk tiering and classification – Develop risk-tiering and classification criteria that account for the AI use case, the sensitivity of the data involved, and the potential business impact of failure, bias, or misuse

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Due diligence design – Create and maintain AI-specific due diligence questionnaires and assessment criteria covering model design, training data, transparency and explainability, bias mitigation, security, and ongoing monitoring

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Vendor evidence review – Evaluate vendor documentation and control evidence, such as SOC 2 reports, ISO/IEC 42001 certifications, model cards, and impact assessments, and identify gaps that require remediation or compensating controls

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Risk decisions – Determine and communicate the level of risk each AI solution presents, and partner with business owners on remediation, risk acceptance, or alternative approaches

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AI inventory and shadow AI – Maintain an inventory of third-party AI in use across the organization, working to surface and govern shadow AI

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Contractual requirements – Partner with Procurement and Legal to define contractual requirements for AI vendors, including disclosure of AI use, data handling, and responsible-AI commitments

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Continuous monitoring – Establish continuous monitoring and periodic reassessment of AI vendors across the relationship lifecycle, from onboarding through offboarding

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Metrics and regulatory tracking – Develop program metrics, reporting, and dashboards that demonstrate coverage, maturity, and risk posture to leadership, and track evolving AI regulations and standards, such as the EU AI Act and the NIST AI Risk Management Framework, that affect third-party AI

Who You Are (Qualifications & Skills)
Minimum Qualifications

- 6+ years of experience in third-party or vendor risk management, IT or security risk, compliance, or a related governance function

- Demonstrated experience building or substantially maturing a vendor risk or governance program, including intake and risk-tiering processes

- Strong knowledge of third-party and vendor risk management principles and lifecycle, from intake and due diligence through monitoring and offboarding

- Familiarity with AI and machine learning concepts and the risks they raise, including bias, transparency, data privacy, security, and model reliability

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