Role Description
Develop and oversee responsible artificial intelligence, AI governance, and model-risk programmes that ensure AI systems are safe, fair, transparent, reliable, and compliant with applicable regulations. This role is responsible for establishing governance frameworks and controls throughout the complete AI and machine learning lifecycle.
The successful candidate will assess AI use cases, identify model risks, establish approval requirements, and define standards for model development, validation, deployment, monitoring, and retirement. Responsibilities include conducting risk assessments, reviewing model documentation, monitoring control effectiveness, and coordinating remediation activities.
The role requires close collaboration with data science, machine learning engineering, legal, compliance, cybersecurity, privacy, internal audit, risk management, product, and senior leadership teams. Additional responsibilities include maintaining AI inventories, developing policies, supporting regulatory reviews, delivering employee training, and advising teams on responsible AI implementation.
For leadership responsibilities, the position may chair AI governance committees, manage model-risk teams, establish enterprise risk thresholds, oversee independent model validation, and report significant AI risks and governance matters to executive leadership or the board.
Qualifications
- Bachelor’s, master’s, or PhD in Artificial Intelligence, Computer Science, Data Science, Statistics, Risk Management, Law, Ethics, or a related discipline.
- Experience in responsible AI, AI governance, model risk management, machine learning validation, compliance, or technology risk.
- Strong understanding of machine learning models, generative AI, large language models, model-development lifecycles, and AI system architecture.
- Knowledge of AI fairness, transparency, explainability, accountability, privacy, security, robustness, and human oversight.
- Experience developing AI policies, governance frameworks, risk-assessment methodologies, and control standards.
- Ability to evaluate model design, data quality, performance, limitations, potential bias, and unintended outcomes.
- Experience maintaining model inventories, risk classifications, validation records, approvals, and monitoring documentation.
- Familiarity with relevant AI regulations, regulatory guidance, industry standards, and risk-management frameworks.
- Experience conducting model validation, control testing, regulatory assessments, or internal audits is beneficial.
- Ability to translate technical, legal, and ethical requirements into practical operational controls.
- Strong analytical, critical-thinking, decision-making, and problem-solving abilities.
- Excellent communication, policy-writing, presentation, and senior stakeholder-management skills.
- Ability to challenge technical teams constructively and communicate complex risks to non-technical audiences.
- Professional certification or advanced training in AI governance, model risk, data privacy, compliance, cybersecurity, or risk management is advantageous.