The Co-operative Bank of Kenya Limited is incorporated in Kenya under the Company Act and is also licensed to do the business of banking under the Banking Act. The Bank was initially registered under the Co-operative Societies Act at the point of founding in 1965. This status was retained up to and until June 27th 2008 when the Banks Special General Meeti...

The Role

The successful jobholder will be expected to:

• Define and maintain risk-based test strategies and plans for AI-enabled applications, data pipelines, integrations and platform services, with traceability to agreed requirements and acceptance criteria.
• 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 that address relevant business, technical and AI risks, while protecting sensitive and personal data and collaborating with ICT Security and AI Risk and Compliance.
• Integrate automated quality gates into delivery pipelines and ensure defects, failed controls and release risks are 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 the resulting test evidence in line with audit and governance requirements; business acceptance remains with the designated business owner.
• Maintain objective test evidence, track defects and verify fixes, working with responsible teams to resolve issues. Report residual risks and provide an independent quality assessment without assuming business or risk approval authority.
• Review post-release quality with engineering and support teams, and use the findings to improve test coverage, automation and quality practices across AI delivery squads.

Qualifications, Skills & Attributes

The successful jobholder will be required to possess the following qualifications, skills and attributes:

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

• Define and maintain risk-based test strategies and plans for AI-enabled applications, data pipelines, integrations and platform services, with traceability to agreed requirements and acceptance criteria.
• 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 that address relevant business, technical and AI risks, while protecting sensitive and personal data and collaborating with ICT Security and AI Risk and Compliance.
• Integrate automated quality gates into delivery pipelines and ensure defects, failed controls and release risks are 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 the resulting test evidence in line with audit and governance requirements; business acceptance remains with the designated business owner.
• Maintain objective test evidence, track defects and verify fixes, working with responsible teams to resolve issues. Report residual risks and provide an independent quality assessment without assuming business or risk approval authority.
• Review post-release quality with engineering and support teams, and use the findings to improve test coverage, automation and quality practices across AI delivery squads.

• 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.

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