Senior Specialist -AI Solutions Quality Assurance - Acceptance Testing and User Experience Quality
Role Overview
Implement acceptance testing and user experience quality assurance for RTA AI solutions before and after deployment.
Safeguard model quality, fairness, compliance, and reliability through UAT, validation, monitoring, and continuous improvement.
Key Skills for This Role
Full Job Posting
Role purpose
Implement acceptance testing and user experience quality assurance for RTA AI solutions before and after deployment.
Safeguard model quality, fairness, compliance, and reliability through UAT, validation, monitoring, and continuous improvement.
Strategy and governance
- Define risk-based quality requirements covering traceability, explainability, fairness, privacy, security, safety, and reliability.
- Establish lifecycle quality standards and control points; review technical designs for quality and compliance.
- Document audit and regulatory requirements and update AI quality policies, standards, and engineering specifications.
Testing and operations
- Execute UAT with development and operations teams to verify functionality, performance, and user experience before production.
- Validate and verify models, test accuracy, set acceptance thresholds, and perform pre-launch technical verification.
- Conduct fairness and bias reviews before and after launch and issue launch recommendations.
- Set post-launch KPIs, drift thresholds, incident boundaries, revalidation and retraining triggers, and deactivation protocols.
- Monitor compliance, follow up on corrective actions, audit partner evidence, and maintain documentation for oversight.
Product and process improvement
- Develop AI-specific UX/UI guidelines and provide quality, acceptance-testing, and UX guidance and materials to stakeholders.
- Analyze quality metrics and targets, identify improvements, prepare reports and dashboards, and benchmark practices against emerging standards.
Working conditions
- Work primarily in the RTA office, with occasional visits to production sites or partner facilities for verification, device inspection, or coordination.
Qualifications
- Bachelor's degree in Computer Science, Information Systems, Software Engineering, Data Science, or a related technical field.
- 6+ years of relevant experience with a master's degree, or 8+ years with a bachelor's degree, in AI/ML QA, software acceptance testing, or model validation.
- Professional certification in software testing, AI/ML engineering, or quality management, such as ISTQB Advanced Level, ISO 9001, or CMMI.
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