Chief Specialist - AI Solutions Quality Assurance - Auditing and Compliance

Roads and Transport Authority; Dubai; On-site; AED 350,000 - 550,000; Full time

Job summary

Roads and Transport Authority in Dubai seeks a senior leader to head AI quality assurance, auditing, and compliance across all AI solutions. You will establish governance standards, risk-based gates, and model validation frameworks aligned with Dubai Data Law and global AI governance best practices.

You will coordinate with regulatory authorities, manage pre-launch verifications, fairness reviews, post-launch monitoring, and governance dashboards to ensure accountability, transparency, and

Qualifications

• Experience leading AI quality assurance, audit, and compliance programs.
• Proven ability to define risk-based quality gates across AI lifecycles.
• Strong knowledge of AI governance standards and regulatory requirements.

Responsibilities

• Develop and maintain AI QA and Compliance Framework aligned with Dubai Data Law and global best practices.
• Define risk-based quality requirements covering traceability, explainability, fairness, privacy, security, safety, and reliability.
• Coordinate with regulators and supervisory authorities to ensure compliance of AI portfolio.
• Lead pre-launch verification and post-launch reviews, issuing go/no-go decisions and remediation actions.
• Monitor model performance, establish KPIs, drift thresholds, incident handling, and governance dashboards.

Skills

AI governance

Model lifecycle management

Fairness assessment

Explainable AI (XAI)

Regulatory compliance

Education

Bachelor degree in Computer Science / AI / Data Science / Information Systems

Tools

AI governance frameworks

Regulatory standards

Job description

Lead the quality assurance, auditing, and compliance function for all AI solutions across RTA, ensuring strict adherence to governance standards, ethical principles, and regulatory requirements. Establish and enforce risk-based quality gates, fairness assessments, and model validation frameworks across the full AIlifecycle. Serve as the authoritative compliance and audit voice for AI deployments, safeguarding organizational integrity and stakeholder trust.

Roles and Responsibilities:

• Develop, maintain, and periodically update the AI Solutions Quality Assurance and Compliance Framework,embedding governance standards, ethical principles, and technical specifications in alignment with RTA regulatory requirements, Dubai Data Law, and global AI governance best practices.
• Define risk-based quality requirements for all AI solutions, encompassing traceability, explainability, fairness, privacy, security, safety, and reliability criteria, and establish quality standards and control gates (approval checkpoints) mapped to each stage of the AI lifecycle.
• Identify and consolidate AI compliance and audit requirements in coordination with relevant regulatory and supervisory authorities, ensuring RTA's AI portfolio meets all mandated standards and emerging legislative expectations.
• Shape the AI quality and compliance roadmap by benchmarking against industry-leadin frameworks (e.g.,
• ISO/IEC 42001, NIST AI RMF, EU AI Act) and integrating lessons learned into evolving institutional policies.

Operations:

• Execute pre-launch technical verification of AI solutions, including validation of technical documentation completeness, resolution of quality and assurance findings, and issuance of go/no-go recommendations for deployment.
• Conduct fairness, bias, and ethical reviews of AI solutions and significant model changes both pre- and postlaunch, documenting findings, issuing compliance verdicts, and recommending remediation actions before operational release.
• Review technical designs of AI solutions from a quality, compliance, and standard conformance perspective, verifying alignment with institutional requirements in coordination with relevant stakeholders and departments.
• Apply AI model quality assurance frameworks to monitor, validate, and verify models including defining validation methods, acceptance thresholds, evidence requirements, and independent review roles throughout the model lifecycle.
• Monitor post-launch model performance, defining KPIs, model drift thresholds, incident boundaries, and escalation protocols to ensure sustained compliance and operational reliability.
• Define and enforce model change governance requirements, including re-validation, re-training triggers, change management procedures, and model deactivation protocols when performance or compliance thresholds are breached.
• Monitor compliance with AI quality standards and controls by internal development teams, implementation partners, and service providers, tracking and following up on corrective actions when non compliance is identified.
• Exercise and enforce contractual audit rights over implementation partners and service providers developing or operating AI models, including access to model documentation, data lineage, and evaluation evidence for independent verification.
• Manage audit evidence, regulatory responses, and findings related to AI oversight and compliance tasks, maintaining a centralized repository of audit trails and remediation records.
• Follow and apply User Acceptance Testing (UAT) guidelines in coordination with the Development and Operations team, ensuring all AI solutions meet defined acceptance criteria before production deployment.

Product/Process Improvement

• Develop and implement UX/UI guidelines specific to AI solutions in coordination with the Development and Operations team, ensuring transparent, accessible, and user-centric AI interactions.
• Raise awareness and provide expert guidance on AI quality, compliance, and governance standards to business owners, technical teams, and implementation partners, supplying templates, checklists, and reference materials for consistent application.
• Evaluate and introduce automated compliance-checking tools, model validation pipelines, and governance dashboards to optimize the efficiency and integrity of the AI quality assurance process.
• Research emerging AI governance technologies, regulatory developments, and industry practices,integrating insights into updated frameworks and recommending process enhancements to leadership.

Qualifications

• Education: Bachelor degree in Computer Science, Artificial Intelligence, Data Science, Information Systems, or a closely related technical field. A Bachelor's degree in the same disciplines is acceptable with additional relevant professional experience.
• Experience: 9 years of progressive professional experience for Master's degree holders; 11 years for Bachelor's degree holders.
• Qualification:Demonstrated expertise in AI model lifecycle management, fairness and bias assessment methodologies, explainable AI (XAI) frameworks, and regulatory compliance standards fo machine learning systems.