Roads and Transport Authority · Dubai, AE
Posted:
Job Purpose : Implement acceptance testing and user experience quality assurance for RTA's AI solutions, ensuring all models and systems meet defined quality gates, fairness standards, and regulatory requirements before and after deployment. Design and enforce UAT protocols, UX guidelines, and model validation frameworks in coordination with development and operations teams. Drive continuous quality improvement through post-launch monitoring, compliance audits, and stakeholder engagement to safeguard the integrity and reliability of RTA's AI portfolio. Roles and Responsibilities: Strategy Define and maintain risk-based quality requirements for AI solutions, encompassing traceability, explainability, fairness, privacy, security, safety, and reliability, in alignment with RTA's AI governance framework and applicable regulatory mandates. Establish quality standards and control points (approval gates) across each stage of the AI model lifecycle, ensuring structured progression from development through deployment and post-launch operations. Review technical designs of AI solutions from a quality and compliance perspective, verifying alignment with institutional standards and organizational requirements in coordination with relevant stakeholders and departments. Identify and document compliance and audit requirements related to AI solutions in coordination with relevant regulatory and oversight bodies within RTA, ensuring all quality controls are mapped to regulatory expectations. Develop and periodically update AI quality policies, technical standards, and engineering specifications to reflect evolving best practices, regulatory changes, and organizational strategic priorities. Operations: Execute User Acceptance Testing (UAT) for AI solutions following established UAT guidelines, coordinating closely with development and operations teams to validate functional correctness, performance, and user experience before production deployment. Apply AI model quality assurance frameworks, conducting model validation, verification, and accuracy testing, including defining acceptance thresholds, evidence requirements, and independent review roles for each model release. Perform 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 production activation. Conduct fairness and bias reviews of AI solutions and significant model changes before and after launch, issuing formal results and launch recommendations to the supervising manager. Define and implement post-launch model performance monitoring requirements, including key performance indicators, model drift detection thresholds, and incident boundary definitions to ensure sustained solution quality. Define model change governance requirements, including re-validation, re-training triggers, change management procedures, and model deactivation protocols when quality thresholds are breached. Monitor compliance with AI quality standards and controls by internal teams, implementation partners, and service providers, tracking and following up on corrective actions when non-compliance is identified. Exercise contractual audit rights over implementation partners and service provider developing or operating AI models, including access to model documentation, data lineage, and evaluation evidence for independent verification. Manage audit evidence, responses, and findings related to AI quality assurance and regulatory oversight tasks, ensuring timely and complete documentation for internal and external audits. Perform any additional tasks and responsibilities related to the role as assigned by the supervising manager. Product/Process Improvement Develop and implement user experience and user interface guidelines specific to AI solutions, coordinating with development and operations teams to ensure consistent, accessible, and intuitive interaction patterns across RTA's AI portfolio. Provide guidance and awareness on AI quality standards, acceptance testing methodologies, and UX best practices to business owners, technical teams, and implementation partners, supplying necessary templates, checklists, and reference materials. Analyze AI solution quality metrics against defined targets, conducting periodic reviews to identify opportunities for process improvement, testing methodology enhancement, or UX optimization. Prepare analytical reports and quality dashboards summarizing acceptance testing outcomes, defect trends,model performance data, and compliance status for submission to the department manager to support data-driven quality decisions. Benchmark RTA's AI quality assurance practices against global best practices and emerging standards,recommending enhancements to testing frameworks, UX evaluation methods, and model validation approaches. Physical Working Conditions: Perform quality assurance and acceptance testing tasks primarily in the RTA office environment, with occasional on-site visits to production locations or partner facilities for system verification, device inspection, or stakeholder coordination, ensuring adherence to safety protocols and quality measures during field activities. Qualifications : Education: Bachelor's degree in Computer Science, Information Systems, Software Engineering, Data Science, or a related technical field Experience: 6+ years of progressive experience in AI/ ML quality assurance, software acceptance testing, or model validation in case of a Master's degree; 8+ years in case of a Bachelor's degree Qualification: Professional certification in software testing (e.g., ISTQB Advanced Level), AI/ML engineering, or quality management(e.g., ISO 9001, CMMI)