Role description:
We are looking for an experienced AI Test Leads (AI Foundry) to ensure AI and Copilot solutions are safe, reliable and compliant, covering both traditional QA and AIspecific risks (bias, hallucination, explainability).
The role defines assurance methods, quality gates and postdeployment monitoring to meet internal policy and regulator expectations.
Key responsibilities:
• Design and manage the enterprise testing strategy for AI/Copilot, blending traditional QA with AIspecific methods.
• Define test approaches for functional, performance, accuracy, reliability, ethical compliance and bias detection.
• Establish modelevaluation techniques (prompt variability, edgecase simulation, output consistency, scenario reasoning).
• Validate explainability, traceability and safety controls against policy and regulatory requirements.
• Evaluate and test humanintheloop workflows and decision checkpoints for appropriate oversight.
• Embed quality gates in iterative delivery, preventing progression without assurance evidence.
• Develop and maintain specialised test datasets, including adversarial, lowquality, domainspecific and edgecase inputs, to rigorously challenge model robustness and identify systemic weaknesses.
• Provide AI test engineering support to delivery squads, advising on modelreadiness criteria, testability risks, and quality implications of design decisions, ensuring solutions are verifiable throughout the lifecycle.
• Define and run postdeployment validation, drift detection, incident triage and continuous modelmonitoring.
• Partner with Risk, Legal, Security and Compliance teams to meet control frameworks and audit standards.
• Provide inputs to risk/impact assessments, policy adherence checks and governance submissions.
• Lead incident investigations for unexpected AI behaviours, conducting deepdive rootcause analysis across data quality, model logic, prompt flows, integration layers and humanintheloop steps; identify systemic failure points, recommend corrective actions, and drive endtoend remediation to prevent recurrence.
• Maintain test documentation, evaluation logs, datasets and reproducible evidence for audit.
• Uplift AI testing capability across teams through standards, templates, training and handson support.
• Champion continuous improvement of AI assurance, evaluating new testing tooling (LLMmonitoring, biasscanners, promptdiff tools, synthetic data generators) and maturing standards as organisational AI adoption scales.
• Ensure responsible AI principles (e.g., transparency, explainability, ISO42001) are incorporated into all development.
• Provide insight to support business cases, investment decisions, risk assessments, and prioritisation discussions at AI governance forums.
• Managing escalations supporting the wider Data & AI Leadership team.
Key skills/knowledge/experience:
Functional/Technical (Role Specific)
Essential
• Higher education qualification (or equivalent experience) in Ethics, Law, Risk Management, Social Sciences, Data/Computer Science or relevant field
• Experience with designing and leading testing for complex digital or datadriven systems, including multicomponent architectures, APIintegrated platforms, eventdriven workflows and systems operating under regulatory or highassurance constraints.
• Clear understanding of AIspecific risks such as hallucinations, bias, drift, explainability gaps, safety breaches and misuse pathways, paired with the ability to design targeted tests that uncover model blind spots and systemic weaknesses.
• Knowledge of modelevaluation techniques, prompttesting strategies and scenariobased testing approaches, including stresstesting prompts, adversarial input creation, failuremode exploration and behaviourdriven evaluation.
• Familiarity with governance, audit and regulatory standards for AI, data and digital services, ensuring testing evidence aligns with internal risk frameworks, ISO42001 controls, Responsible AI policies and external regulatory expectations.
• Experience developing structured QA strategies that integrate traditional and AIspecific assurance, mapping out test plans, riskbased prioritisation, acceptance criteria, modelreadiness thresholds and quality gates aligned to lifecycle stages.
• Ability to define and execute test plans across functional, nonfunctional, ethical and performance dimensions, validating accuracy, latency, robustness, security, fairness, reliability and userjourney consistency.
• Strong analytical mindset with the ability to identify root causes of defects or unexpected AI behaviour, performing deepdive diagnostics across data pipelines, vector stores, prompt flows, orchestration logic and humanintheloop checkpoints.
• Experience with postdeployment monitoring, drift detection and continuous validation, designing alerts, retraining triggers, performance thresholds and evaluation cadences to maintain longterm model integrity.
• Comfortable learning and adapting to emerging AI technologies and engineering patterns.
• Excellent stakeholder management and communication skills, including seniorlevel engagement.
• Commercial awareness and a valuedriven mindset.
• Use of professional networks and external influencers with clear evidence of learning and development to build and maintain skills and expertise.
Sector (desirable)
• Understanding of financial services industry, markets and competitors
• Understanding of how financial services organisations operate and the associated regulatory environment, or other regulated industries
• Awareness of the Mutual Sector and the needs and interests of Members.
Commercial
• Ability to work with autonomy and make operational decisions
• Experience of delivering organisational change
• Understanding of related functions and/or services outside of the role’s direct remit.
• Experience of managing a set of internal and external stakeholder relationships