Chief Specialist - AI Solutions Quality Assurance (Model Validation)

About the role

The Chief Specialist - AI Solutions Quality Assurance establishes and enforces quality standards, governance frameworks and compliance controls for AI solutions across the full model lifecycle. The role defines risk-based quality gates, validation protocols and fairness criteria so that AI deployments meet regulatory, ethical and operational excellence benchmarks, and drives continuous improvement through technical verification, post-launch monitoring and cross-functional collaboration.

Key responsibilities

• Develop and update the AI quality assurance framework, including standards, technical specifications, engineering specifications and approval gate criteria aligned with AI governance policies and regulatory requirements.
• Define risk-based quality requirements covering traceability, explainability, fairness, privacy, security, safety and reliability, and prepare quality policies, procedures, guidelines and templates for approval.
• Identify compliance and audit requirements with regulators and governance teams, run quarterly risk reviews of AI quality processes and raise mitigation recommendations.
• Perform pre-launch technical verification, fairness and bias reviews, and UAT coordination with Development and Operations, issuing go/no-go decisions.
• Define post-launch monitoring (KPIs, drift detection, escalation) and model change governance (re-validation, retraining, decommissioning).
• Monitor compliance by internal teams, partners and service providers, exercise contractual audit rights and manage audit evidence and responses.
• Raise awareness of AI quality standards among business owners and technical teams, and provide templates and toolkits.
• Provide clerical support (scanning, archiving) and coordinate with HR on employee requests such as leaves, training and certificates.
• Develop UX/UI guidelines for AI solutions, prepare continuous improvement plans, analyse quality surveys and audit findings, and research emerging QA methodologies and automated validation tools.

Required profile

• Bachelor's degree in Computer Science, Information Systems or a related technical field; minimum 11 years of experience in AI/ML quality assurance, model governance or related fields.
• Master's degree allows a minimum of 9 years of experience in the same fields.
• A professional certification in AI governance, data governance (e.g. DAMA-CDGA) or quality management (e.g. CQI, ISO 9001 Lead Auditor) is advantageous.