AI Governance Head

 JOB PURPOSE: 
To lead the enterprise AI Governance function, establishing the frameworks, standards, policies, and oversight mechanisms required to ensure that AI systems deployed across the bank are governed responsibly, transparently, and in full compliance with regulatory expectations. The role is accountable for designing and operating the enterprise AI governance operating model, maintaining the AI model inventory, governing the AI model lifecycle, and driving responsible AI practices across all AI initiatives.

The Head of AI Governance serves as the bank’s principal authority on AI governance standards, working across AI Engineering, Data Science, Risk, Compliance, and business functions to ensure that AI capabilities are built and deployed within a robust, auditable, and regulatorily compliant governance framework that keeps pace with both the bank’s growing AI portfolio and the evolving CBE regulatory landscape.

KEY ACCOUNTABILITIES: 

Roles and Responsibilities
1.    Define and implement the enterprise AI governance framework, including AI model governance policies, accountability structures, decision rights, tiered control mechanisms, and the operating model that governs the responsible development and deployment of AI across the bank.
2.    Establish and maintain the enterprise AI model inventory and registry, ensuring all AI systems including machine learning models, GenAI solutions, LLM-based applications, intelligent automation pipelines, and automated decision systems are inventoried, risk-tiered, and subject to governance controls proportionate to their risk profile.
3.    Design and operate the AI model lifecycle governance process, covering model submission, risk assessment, validation, approval, deployment authorization, production monitoring, periodic review, and retirement, applying consistent standards across all AI initiatives regardless of originating team.
4.    Lead AI model risk assessments for new and existing AI systems, evaluating risks related to model performance, bias, fairness, explainability, data quality, security, and operational resilience, and defining governance requirements appropriate to each model’s risk tier and business impact.
5.    Develop and maintain responsible AI standards and guidelines, including requirements for model explainability, fairness testing, bias detection and mitigation, human-in-the-loop controls, and ethical use of AI, and ensure these are operationally embedded in the AI development lifecycle from design through to deployment and monitoring.
6.    Establish and enforce AI regulatory compliance standards aligned with CBE guidance and applicable international AI governance frameworks and emerging regulatory expectations, ensuring the bank’s AI practices satisfy supervisory requirements for transparency, accountability, and control.
7.    Operate AI governance forums and oversight committees, facilitating structured review of AI model submissions, model change requests, high-risk deployments, and governance exception requests, maintaining formal and auditable records of all governance decisions.
8.    Collaborate with AI Engineering and Data Science teams to embed governance controls into the AI development and deployment workflow, ensuring governance requirements are operationalized in a practical and delivery-compatible manner.
9.    Partner with Risk and Compliance functions to align AI governance standards with the bank’s model risk management policy, operational risk framework, and data protection obligations, integrating AI governance into the broader enterprise risk and control environment.
10.    Lead AI audit readiness activities, maintaining the documentation, evidence packages, and audit trails required to demonstrate AI governance compliance to internal audit, external auditors, and CBE supervisory examinations.
11.    Manage and develop the AI Governance team, building capability that combines AI technical literacy with risk awareness and regulatory knowledge, and developing a team positioned to govern an increasingly sophisticated and diverse AI landscape.
12.    Contribute to the Enterprise Data & AI Governance strategy by providing AI governance maturity assessments, risk posture updates, and regulatory compliance status that inform governance investment priorities and the bank’s AI ambition.
Compliance
13.    Ensure compliance with all relevant CBE regulations, banking laws, AML regulations and internal CIB policies and code of conduct in order to maintain CIB’s sound legal position and mitigate any potential risks
14.    Supervise the activities and work of subordinates to ensure that all work within a specific area is carried out in an efficient manner and in compliance with the set policies, processes and procedures
Policies, Processes and Procedures
15.    Implement approved department policies, processes, and procedures and monitor adherence so that work is carried out in a controlled manner
16.    Implement the day-to-day operations assigned for the AI Governance department to ensure compliance with the established standards and procedures
 

Qualifications & Experience
        Bachelor’s degree in Computer Science, Artificial Intelligence, Information Systems, Risk Management, or a related field. A Master’s degree in a relevant discipline is preferred.
    8+ years of progressive experience in AI governance, model risk management, AI/ML engineering, or technology risk, with demonstrated expertise in designing and operating AI governance or model risk frameworks in regulated environments.
    Minimum of 3 years in a leadership role overseeing AI governance, model validation, or AI risk management functions.
    Strong working knowledge of AI/ML model development and deployment lifecycles, including model training, evaluation, deployment, monitoring, and the specific governance implications of GenAI and LLM-based solutions.
    Proven experience with responsible AI principles and frameworks, including model explainability methods, fairness testing, bias detection and mitigation techniques, and human-in-the-loop design.

Skills
    Deep expertise in AI governance frameworks, model risk management, and responsible AI principles, with the ability to translate these into practical, operationalizable governance standards and controls across a diverse and evolving AI portfolio.
    Strong technical literacy in AI/ML, including understanding of model types, training and evaluation processes, deployment architectures, MLOps and LLMOps practices, and the specific governance risks posed by GenAI and large language models.
    Proven ability to design and operate AI model lifecycle governance processes, including risk-tiered assessment methodologies, approval workflows, post-deployment monitoring standards, and model retirement protocols.
    Effective cross-functional collaboration skills, with the ability to work constructively with AI Engineering, Data Science, Risk, Compliance, and business teams to embed governance requirements without becoming a barrier to delivery velocity.
    Strong regulatory and risk management acumen, with the ability to interpret evolving AI regulatory guidance, assess its implications for the bank’s AI portfolio, and translate requirements into actionable and proportionate governance changes.
    Sound independent judgment, with the ability to make objective, risk-based governance decisions on AI model submissions, escalations, and governance exceptions, maintaining governance integrity under delivery pressure.
    Strong leadership and team development skills, with experience building governance teams that combine technical AI understanding with risk, regulatory, and ethical governance expertise.