AI - QA Automation Engineer

• Define and maintain risk-based test strategies and plans for AI-enabled applications, data pipelines, integrations and platform services, with traceability to agreed requirements
• Build and maintain automated test suites for APIs, user interfaces, data pipelines, integrations, regression, performance, resilience and security-related quality checks
• Validate the quality, reliability and safety of AI solution behaviour, including relevant safeguards, against approved requirements
• Develop representative test data and scenarios addressing business, technical and AI risks while protecting sensitive and personal data
• Integrate automated quality gates into delivery pipelines and make defects, failed controls and release risks visible before deployment decisions
• Coordinate end-to-end, performance, recovery and production-verification testing with technology teams and support business-led user acceptance testing
• Maintain objective test evidence, track defects, verify fixes and report residual risks with an independent quality view
• Review post-release quality with engineering and support teams and improve test coverage, automation and quality practices across AI delivery squads

Qualifications & Experience:

• Bachelor's degree in Computer Science, Software Engineering, Information Technology, Data Science or a related field
• At least four (4) years of experience in software quality assurance or quality engineering, including at least two (2) years of hands-on test automation
• Demonstrated experience testing APIs, integrations, digital applications and data-intensive systems
• Professional certifications such as ISTQB Foundation Level, Test Automation Engineer, AI Testing or agile testing are an added advantage
• Exposure to AI/ML validation, model testing or data-quality testing is preferred

Skills & Competencies:

• Test strategy, risk-based testing and requirements traceability
• UI, API, integration and data-pipeline automation
• Performance, resilience and security-related testing
• AI/ML quality concepts, metrics, bias and guardrail testing
• CI/CD quality gates and test reporting
• Test-data management and privacy controls
• Defect management and root-cause analysis
• Independent judgement and concise release-risk communication