Chief Specialist - AI Solutions Quality Assurance, Auditing and Compliance

About the role

The Chief Specialist leads the quality assurance, auditing and compliance function for all AI solutions across RTA, ensuring strict adherence to governance standards, ethical principles and regulatory requirements. The role establishes risk-based quality gates, fairness assessments and model validation frameworks across the full AI lifecycle and acts as the authoritative compliance and audit voice for AI deployments.

Key responsibilities

• Develop, maintain and update the AI Quality Assurance and Compliance Framework, aligned with RTA regulations, Dubai Data Law and global AI governance best practices.
• Define risk-based quality requirements (traceability, explainability, fairness, privacy, security, safety, reliability) and set quality standards and approval checkpoints for each stage of the AI lifecycle.
• Consolidate AI compliance and audit requirements with regulators and authorities, and benchmark against frameworks such as ISO/IEC 42001, NIST AI RMF and the EU AI Act.
• Run pre-launch technical verification, fairness, bias and ethical reviews, and issue go/no-go recommendations and compliance verdicts.
• Review technical designs, define model validation methods, acceptance thresholds and independent review roles, and monitor post-launch model performance with KPIs and drift thresholds.
• Enforce model change governance, including re-validation, re-training triggers and deactivation protocols.
• Monitor compliance by internal teams, partners and service providers, exercise contractual audit rights, and manage audit evidence and regulatory responses.
• Follow UAT guidelines and develop AI-specific UX/UI guidelines, raising awareness among business owners and technical teams.
• Evaluate automated compliance-checking tools, model validation pipelines and governance dashboards, and research emerging AI governance technologies.

Required profile

• Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Information Systems or a closely related field; a Master's degree is acceptable with 9 years of progressive experience, while a Bachelor's requires 11 years.
• Demonstrated expertise in AI model lifecycle management, fairness and bias assessment, explainable AI (XAI) frameworks and regulatory compliance standards for machine learning systems.