Senior Technical AI Governance Specialist
Role Overview
The Senior Technical AI Governance Specialist provides deep technical and regulatory expertise to support the design, risk assessment, and governance of AI and ML solutions at enterprise scale. This role is responsible for ensuring AI systems comply with regulatory, security, and risk management standards while enabling responsible innovation across high‑risk and regulated environments, particularly within banking and financial services.
The role acts as a central governance authority, working closely with model owners, architects, cybersecurity teams, and regulators to assess AI risks, oversee validation activities, and operate AI governance frameworks effectively.
Key Responsibilities
AI Governance & Oversight
- Operate and administer day‑to‑day AI governance activities across enterprise AI initiatives
- Support governance forums, including agenda planning, operating cadence, documentation, and follow‑ups
- Maintain centralized AI use‑case intake, inventory management, and lifecycle oversight
- Define and maintain governance standards, RACI models, and approval workflows
Model Risk, Testing & Validation
- Review and challenge AI/ML design decisions across the full model lifecycle
- Oversee and assess model testing and validation outputs, including:
- Model performance testing
- Validation and robustness testing
- Bias and adversarial testing
- Vulnerability and resilience testing
- Monitor key risk indicators (KRIs) and drive remediation plans for high‑risk AI use cases
- Support escalation and stop‑decision processes where required
Architecture, Security & Resilience
- Coordinate enterprise AI architecture, platform, and model governance reviews
- Partner with cybersecurity, risk, and resilience teams to ensure compliance with security and control standards
- Support security, resilience, and compliance validation for AI platforms and solutions
Regulatory, Audit & Compliance
- Support regulatory interactions, audits, and supervisory reviews related to AI and model governance
- Prepare, manage, and maintain governance evidence, controls documentation, and approvals
- Coordinate responses to internal and external audit findings
- Ensure ongoing compliance with banking, regulatory, and governance requirements
Stakeholder & Program Management
- Coordinate with model owners, delivery teams, and cross‑functional stakeholders
- Support program and portfolio‑level governance for AI initiatives
- Communicate effectively with executives, regulators, and senior risk stakeholders
- Drive issue identification, risk escalation, and resolution management
Required Capabilities & Skills
Technical & AI Expertise
- Strong experience in AI/ML and model lifecycle management
- Deep understanding of model testing, validation, and performance monitoring
- Enterprise AI architecture and platform literacy
- Cybersecurity, resilience, and technology risk awareness
Governance, Risk & Compliance
- Banking and regulatory AI governance experience
- Model risk management and governance oversight
- Audit management and regulatory evidence preparation
- Risk classification, approval workflows, and escalation management
Program & Stakeholder Management
- Program and portfolio governance experience
- Cross‑functional and senior stakeholder management
- Executive‑level and regulatory communication skills
- Governance forum and operating model management
Analytical & Business Skills
- Strong analytical and quantitative discipline
- Ability to assess ROI and evaluate AI business cases
- Process design, standardization, and documentation expertise
Control Activities Supported
- Centralized AI use‑case intake and inventory management
- High‑risk AI use‑case classification, approval, and oversight
- Governance control definition and evidence management
- Architecture, security, and risk review coordination
- Ongoing monitoring of high‑risk AI deployments and remediation actions
Purpose of the Team
The AI Governance team provides the technical, regulatory, and risk depth required to effectively challenge AI design decisions, assess and mitigate enterprise AI risk, and operate scalable AI governance frameworks. The team’s role is reinforced by increasing AI adoption, regulatory expectations, staffing demands, and findings from internal and external audits.
