New opportunity

Technical Test Services Evaluation Manager, AI Tester Accreditation Programme (AI TAP)

Infocomm Media Development Authority (IMDA) · Singapore

About this role

The AI Verify Foundation, a subsidiary of the Infocomm Media Development Authority (IMDA), is seeking a Technical Test Services Evaluation Manager to lead the AI Tester Accreditation Programme (AI TAP). This role is central to advancing trustworthy AI in Singapore by accrediting testing service firms that assess AI systems for safety, reliability, and governance risks. Responsibilities: Lead the end-to-end accreditation lifecycle for AI testing service providers, including application reviews, technical evaluations, and annual renewals. Evaluate applicant firms against defined technical competency criteria and established frameworks such as the IMDA Starter Kit for Testing LLM-Based Applications. Develop and maintain accreditation documentation, rubrics, and guidelines to ensure rigorous standards. Engage with industry stakeholders, developers, and deployers to refine assessment scopes and monitor developments in AI testing methodologies and governance. Qualifications: Bachelor's degree in Computer Science, AI, IT, Engineering, or a related field. Minimum of 3-5 years of experience in GenAI/Agentic AI systems evaluation, technical testing, or software quality assurance. Strong knowledge of AI development lifecycles and safety considerations. Experience in designing or managing accreditation, certification, or audit programmes is highly desirable. Familiarity with AI governance frameworks and ISO/IEC AI standards is required. Candidates should possess strong analytical skills, excellent communication abilities for engaging with technical and non-technical stakeholders, and commercial awareness of the AI testing market. Professional certifications in AI governance (e.g., AIGP) or GenAI testing are considered advantageous.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Generative AILLM evaluationAI governanceSoftware quality assuranceTechnical testingAI safetyAI reliabilityAccreditation managementAudit programmesStakeholder engagement

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

End-to-End Accreditation Lifecycle Management
The core responsibility is managing the full lifecycle of AI testing service providers, from initial application review and technical evaluation to final accreditation decisions and annual renewals. Success requires maintaining rigorous, consistent standards across all applicants.
Technical Evaluation of AI Testing Competencies
You must evaluate applicant firms against established technical frameworks, such as the IMDA Starter Kit for Testing LLM-Based Applications. This requires deep knowledge of GenAI/Agentic AI development lifecycles and the ability to assess whether a firm's technical capabilities meet safety, reliability, and governance requirements.
Stakeholder Engagement and Ecosystem Development
The role involves bridging the gap between testing service providers, AI developers, and policy makers. You must actively engage with these groups to refine assessment scopes and support the growth of a trusted AI assurance marketplace in Singapore.
Standardization and Documentation
You are responsible for developing and maintaining accreditation rubrics, guidelines, and documentation. This ensures that the program remains credible and that evaluation processes are transparent and repeatable as the AI landscape evolves.

Likely challenges to think through

Navigating a Nascent and Evolving Program
As the AI TAP is a new initiative, processes and standards are still being shaped. You will face the challenge of operating in an environment where you must simultaneously execute current evaluations while helping to define the future framework, requiring high adaptability and self-motivation.
Balancing Technical Rigor with Commercial Awareness
You must assess complex technical claims from firms while understanding the commercial realities of the AI testing market. The difficulty lies in maintaining high safety and governance standards without creating undue barriers that stifle the growth of the testing ecosystem.
Communicating Complex Technical Concepts to Diverse Stakeholders
You will need to translate highly technical AI evaluation findings into clear, actionable reports for both technical experts and non-technical stakeholders, including C-suite executives and policy makers. Prepare by practicing how to explain the 'why' behind technical accreditation decisions in a business context.

Practice your answers

Technical practice

Evaluating LLM-based applications for safety and reliability

What to coverDiscuss the key metrics and methodologies used to assess LLM safety, such as hallucination rates, bias detection, and robustness against prompt injection. Suggestion: Familiarize yourself with the IMDA Starter Kit for Testing LLM-Based Applications to demonstrate alignment with local standards.

Managing an accreditation or audit lifecycle

What to coverExplain the steps involved in managing an end-to-end accreditation process, from initial application review to technical evaluation and annual renewal. Focus on how you maintain consistency and rigour through documentation and rubrics.

Applying AI governance frameworks and standards

What to coverDescribe how you would map a firm's technical testing capabilities against established frameworks like ISO/IEC AI standards. Suggestion: Prepare to discuss the practical challenges of translating high-level governance principles into actionable technical audit criteria.

Assessing technical competency in AI testing firms

What to coverHow do you verify that a third-party firm possesses the necessary operational readiness and technical expertise to perform AI audits? Focus on the evidence you would require, such as their testing methodologies, toolsets, and past project experience.

Monitoring developments in AI testing methodologies

What to coverHow do you stay current with the rapidly evolving landscape of GenAI and Agentic AI testing? Explain your process for updating assessment guidelines to ensure they remain relevant as new AI risks emerge.

Balancing rigour with market adoption

What to coverDiscuss the trade-offs between setting highly stringent accreditation standards and ensuring the growth of a healthy AI assurance marketplace. How do you ensure the programme remains credible without stifling industry innovation?

Technical documentation and reporting

What to coverDescribe your approach to creating clear, well-structured evaluation reports that communicate complex technical findings to non-technical stakeholders. Emphasize the importance of objectivity and evidence-based decision-making.

Understanding the AI assurance ecosystem

What to coverExplain your perspective on the current AI testing services market in Singapore and the role of independent assessment in building trust. Suggestion: Research the AI Verify Foundation's existing tools like Project Moonshot and the AI Verify Toolkit to understand the current ecosystem.

Behavioural practice

Engaging diverse stakeholders

What to coverDescribe a situation where you had to explain complex technical risks to a non-technical stakeholder, such as a C-suite executive or policy maker. Focus on how you adapted your communication style to ensure understanding and alignment.

Navigating ambiguity in a new programme

What to coverProvide an example of how you have operated effectively in a nascent environment where processes and standards were still being defined. Highlight your initiative in shaping or improving those frameworks.

Managing conflicting priorities in accreditation

What to coverTell me about a time you had to make a difficult decision regarding an accreditation application where the firm met some but not all criteria. How did you balance the need for rigour with the goal of supporting the ecosystem?

Collaborating across organizational boundaries

What to coverDescribe a project where you had to work with external industry partners and government agencies to achieve a shared goal. How did you manage differing expectations and ensure the project remained on track?

Sharpen your knowledge

Topics to prepare

AI Governance Frameworks and Standards
Review ISO/IEC AI standards and the IMDA AI testing guidelines. Exercise: Map the requirements of a specific ISO/IEC standard to the IMDA Starter Kit for Testing LLM-Based Applications to understand how to evaluate compliance in a professional setting.
Accreditation and Audit Lifecycle Management
Study the principles of designing certification or audit programmes. Exercise: Draft a mock evaluation rubric for an AI testing firm, focusing on how to objectively measure technical competency against predefined safety and reliability criteria.
Generative AI and LLM Safety Considerations
Deepen knowledge of safety, reliability, and governance risks in GenAI and Agentic AI. Exercise: Analyze a hypothetical LLM application failure and identify which specific safety metrics or testing methodologies should have been applied to mitigate the risk.

Skills to revise

Technical Evaluation and Reporting
Practice writing clear, structured technical evaluation reports. Exercise: Create a summary report based on a simulated audit of a testing firm, ensuring the language is accessible to both technical experts and non-technical stakeholders.
Stakeholder Engagement
Develop strategies for communicating complex technical findings to diverse audiences. Exercise: Prepare a presentation outline for a C-suite executive explaining the business value and risk mitigation benefits of the AI Tester Accreditation Programme.
Software Quality Assurance for AI
Apply traditional QA principles to AI systems. Exercise: Compare and contrast standard software testing lifecycles with the unique requirements of testing LLM-based applications, specifically focusing on non-deterministic outputs.

Your preparation checklist

Master the IMDA AI Testing Frameworks
Review the IMDA Starter Kit for Testing LLM-Based Applications and the AI Verify Toolkit. Prepare to discuss how you would apply these specific guidelines to evaluate the technical competency of an applicant firm.
Develop an Accreditation Rubric Sample
Draft a mock evaluation rubric or a set of assessment criteria for a hypothetical AI testing service provider. This demonstrates your ability to create rigorous, structured documentation for accreditation programs.
Prepare a Case Study on AI Risk Assessment
Create a brief walkthrough of a past project where you evaluated an AI system for safety, reliability, or governance risks. Highlight your methodology for identifying gaps and your process for communicating these findings to stakeholders.
Research the AI Assurance Ecosystem
Synthesize your knowledge of current ISO/IEC AI standards and local Singaporean AI governance initiatives. Be ready to explain how these standards influence the commercial market for AI testing services.
Formulate Strategic Questions for the Interviewer
Prepare questions regarding the current challenges in scaling the AI TAP, how the team balances technical rigor with market adoption, and how the program plans to evolve alongside emerging GenAI and Agentic AI capabilities.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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