AI GRC Architect

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Job Title

AI GRC Architect

About this role

You will own the enterprise AI Governance architecture roadmap and technical implementation for OneTrust and associated GRC platforms, ensuring seamless integration, strong governance controls, and alignment with enterprise risk and compliance objectives. This role defines how Responsible AI policy is translated into scalable technical controls, lifecycle gates, and evidence models across business units.

You will establish target-state architecture for AI intake, risk tiering, control mapping, approval workflows, continuous monitoring, and audit-ready traceability. Partnering with program owners, cyber/risk stakeholders, legal/compliance teams, and platform engineering, you will deliver governed designs that scale from pilot through production with clear operating ownership.

This is a high-impact architecture role with executive visibility and a mandate to reduce governance friction while strengthening enterprise risk posture.

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What you’ll do

• Define and lead enterprise architecture for AI Governance and Cyber GRC workflows, including standardized risk/control/evidence operating models.
• Own end-to-end AI governance lifecycle design: intake, classification, risk tiering, review/approval gates, exception handling, monitoring, and periodic reassessment.
• Translate policy and regulatory obligations into technical control patterns and reusable architecture blueprints deployable across UAT and production.
• Design and govern resilient API integrations across OneTrust, Archer, and broader enterprise systems, including SaaS-to-on-premise and hybrid environment constraints.
• Establish role, group, and organizational segmentation architecture to enforce least-privilege access and scalable governance operations.
• Lead architecture decisions for automation, error handling, observability, incident response, and production support readiness.
• Chair cross-functional architecture reviews and influence risk, compliance, and engineering decision forums on control design and prioritization.
• Mentor engineers and analysts through architecture standards, implementation playbooks, and design quality reviews.

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What success looks like in the first 12 months

• AI governance workflows are standardized and operating consistently across major business domains.
• Manual evidence collection effort is materially reduced through automation and reusable control patterns.
• Assessment cycle times improve with clearer lifecycle gates, ownership, and exception pathways.
• Audit and regulatory traceability are strengthened with high-confidence control-to-evidence lineage.

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Experience you’ll need to have

• 8+ years of combined experience across governance, risk, compliance, cybersecurity engineering, or adjacent technical disciplines in complex enterprise environments.
• 5+ years in enterprise architecture or senior engineering roles delivering security, GRC, compliance, or governance platforms at scale.
• Demonstrated experience designing enterprise AI governance or model risk lifecycle controls from policy through technical implementation.
• Deep integration engineering expertise: REST APIs, OAuth, reliability patterns, dependency governance, and production-grade operational resilience.
• Strong software and scripting capability (for example Python, JavaScript, and API-driven automation) with flexibility to work across unfamiliar stacks when needed.
• Experience translating business and regulatory requirements into control taxonomies, implementation plans, and governed technical workflows.
• Strong executive and technical communication skills, including architecture decision records, stakeholder alignment, and risk tradeoff articulation.
• Proven experience leveraging GRC platforms to operationalize compliance requirements across frameworks including SOC 2, HITRUST, NIST AI RMF, and PCI DSS through automated assessments, continuous control monitoring…