AVP, AI Governance.Artificial Intelligence.MIT

Al Ghurair Investment LLC; United Arab Emirates; On-site; AED 300,000 - 400,000; Full time

Job summary

A prominent investment firm in the United Arab Emirates seeks a Lead for their AI Governance program. This role involves establishing policies and standards, ensuring responsible AI adoption, and working cross-functionally with various teams. The ideal candidate should possess advanced knowledge of risk management concepts, have strong leadership skills, and hold relevant degrees in technology or data science. This position also requires engaging with stakeholders to drive the integration of AI in a secure and compliant manner.

Qualifications

• Familiar with model risk management concepts and Responsible AI principles.
• Experience in audit practices.
• Strong communication for executive engagement.

Responsibilities

• Define and maintain AI Governance Policy and standards.
• Embed policy into SDLC/MLOps and risk frameworks.
• Design governance workflows from intake to sunset.
• Train teams on Responsible AI and conduct awareness campaigns.
• Provide executive dashboards and KPI reporting.

Skills

Risk management concepts

Executive stakeholder management

Policy writing

Training and change management

Education

Bachelor’s/Master’s in Computer Science, Data Science, Engineering, or related

Relevant certifications in risk, audit, or AI governance

Job description

Lead Mashreq’s enterprise-wide AI Governance program to ensure AI and ML (including GenAI/LLMs) are responsible, compliant, secure, fair, explainable, and well-controlled across their lifecycle. Establish policies, standards, and controls; oversee model risk governance; and enable safe, value-creating AI adoption aligned to Mashreq’s strategy, regulatory expectations, and customer trust.

Responsibilities

1. AI Policy & Standards
   • Define, publish, and maintain Mashreq’s AI Governance Policy, standards, and procedures (Responsible AI, model lifecycle, GenAI safety, human-in-the-loop, data & privacy, monitoring).
   • Embed policy into SDLC/MLOps, change management, and risk frameworks.
2. GenAI / LLM Controls
   • Define guardrails for prompt security, data leakage prevention, content safety, output evaluation, and usage logging.
3. Regulatory & Assurance
   • Interpret applicable regulations and guidance; evidence compliance and audit readiness.
4. Risk Management & Security Integration
   • Align AI controls with Cybersecurity, Data Protection, Operational Risk, and Third‑Party Risk.
   • Implement access controls, encryption, privacy‑by‑design, and incident response for AI systems.
5. Governance Operating Model
   • Design RACI and governance workflows, from use‑case intake to go‑live and sunset.
   • Drive adoption of tools (model registry, monitoring dashboards, policy attestations).
6. Stakeholder Enablement & Culture
   • Train teams on Responsible AI; run awareness campaigns; publish guidance/playbooks.
   • Partner with Business, Technology, Data, Risk, Compliance, Legal, and Audit to enable safe innovation.
7. Metrics & Reporting
   • Define KPIs/KRIs (policy adherence, coverage, validation backlog, incidents, bias and drift metrics).
   • Provide executive dashboards and regular reporting to leadership committees.
8. Portfolio Oversight & Prioritization
   • Triage and prioritize AI use cases; ensure value vs. risk trade‑offs and timely approvals.
   • Escalate material risks and recommend risk treatment or go/no‑go decisions.
9. Continuous Improvement

Continuous Improvement

• Benchmark against leading frameworks (e.g., risk management, AI RMF, ISO standards).
• Industrialize governance processes and automate controls.

Operates within Mashreq policies, risk appetite, IT standards, data protection requirements, and applicable regulatory guidance.

Works cross‑functionally with Technology (AI/ML Engineering, Data Platform, Cybersecurity), Risk, Compliance, Legal, Audit, and Business Units; engages external vendors/partners under third‑party risk controls.

Boundaries include approval mandates defined by Risk Committees, budget authority, and change governance.

Knowledge & Experience

Risk & Compliance: Familiar with model risk management concepts, Responsible AI principles, and audit practices.

Leadership & Communication: Executive stakeholder management, committee engagement, policy writing, training and change management.

Education & Certifications: Bachelor’s/Master’s in Computer Science, Data Science, Engineering, or related. Relevant certifications in risk, audit, or AI governance are advantageous.