Senior Specialist - AI Solutions Quality Assurance - Model Validation

Job Purpose Ensure the quality, compliance, and ethical integrity of RTA's AI solutions across the full model lifecycle bydefining risk-based quality standards, executing validation and fairness reviews, and enforcing governance gates. Serve as the primary quality authority for AI deployments, coordinating with development, operations, and implementation partners to verify technical designs, manage UAT, and monitor postlaunch model performance. Drive continuous improvement of AI quality frameworks and regulatory compliance in alignment with RTA's strategic AI roadmap.Roles and Responsibilities Strategy Develop and periodically update AI quality assurance policies, technical standards, and engineering specifications in alignment with RTA's AI governance framework, regulatory requirements, and global best practices.Define risk-based quality requirements for AI solutions encompassing traceability, explainability, fairness, privacy, security, safety, and reliability, ensuring alignment with approved regulatory mandates.Establish quality standards and approval gates (control points) across all AI lifecycle stages, contributing to the department's AI quality strategy and governance roadmap.Benchmark global AI quality assurance frameworks, model validation methodologies, an ethical AI standards to recommend enhancements to RTA's AI governance model and policy architecture.Operations Apply AI model quality assurance frameworks, performing validation, monitoring, and verification of modelsincluding validation methods, acceptance thresholds, evidence requirements, and independent review roles.Execute fairness reviews and material change assessments on AI models before and after launch, issuing go/no-go recommendations and documenting findings for the approval record.Review technical designs of AI solutions from quality and compliance perspectives, verifying alignment withinstitutional standards and relevant requirements in coordination with stakeholders and concerned departments.Perform initial technical verification of AI solutions pre-launch, including completeness of technical documentation, resolution of quality and assurance findings, and clearance for operational deployment.Follow and apply User Acceptance Testing (UAT) guidelines in coordination with the Development and Operations teams, ensuring all acceptance criteria are met before production release.Develop and apply UX/UI guidelines for AI solutions in coordination with the Development and Operations teams, ensuring user-facing AI interfaces meet quality and accessibility standards.Define post-launch model performance monitoring requirements including KPIs, model drift thresholds, incident boundaries, and escalation protocols for ongoing model health.Define model change governance requirements including re-validation, retraining triggers,change management workflows, and model retirement protocols when performance degrades.Monitor compliance with AI quality standards and controls by internal teams, implementation partners, and service providers, tracking and following up on corrective actions for non-compliance.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 forindependent verification.Identify and coordinate AI-related compliance and audit requirements with relevant regulator bodies in liaison with concerned RTA departments, ensuring timely and complete submissions.Manage evidence, responses, and feedback related to audit and regulatory oversight tasks, maintaining a centralized repository of compliance documentation.Raise awareness and provide guidance on AI quality standards, governance practices, and ethical considerations to business owners, technical teams, and implementation partners, supplying templates, checklists, and evidence for implementation.Product / Process Improvement Analyze AI quality metrics, audit findings, and post-launch incident data to identify systemic weaknesses and recommend process enhancements to the department leadership.Develop and refine quality assurance toolkits, checklists, and standardized templates to ensure consistent AI model evaluation across all projects and delivery teams.=Conduct periodic reviews of AI quality frameworks against emerging regulatory requirements, industry best practices, and technological advancements to maintain framework relevance.Prepare management reports on AI quality compliance status, risk exposure, model performance trends, and improvement opportunities for department and agency leadership.

Qualifications

Education: Master's degree in Computer Science,Information Systems, Data Science,Artificial Intelligence, or a related technical fieldExperience: 6+ years of progressive experience in AI/ ML quality assurance, data governance,model validation, or related technicalassurance roles (8+ years in case ofBachelor's degree)Qualification: Professional certification in AI governance, data quality management, or model risk management (e.g., DAMA-DMBOK, AI Governance certifications, CQI)