AI Test Lead (AI Foundry) - 3 Days Work from Office - Leeds or Bradford
WNS (Holdings) Limited (NYSE: WNS), is a leading Business Process Management (BPM) company. We combine our deep industry knowledge with technology and analytics expertise to co-create innovative, digital-led transformational solutions with clients across 10 industries. We enable businesses in Travel, Insurance, Banking and Financial Services, Manufacturing, Retail and Consumer Packaged Goods, Shipping and Logistics, Healthcare, and Utilities to re-imagine their digital future and transform their outcomes with operational excellence. We deliver an entire spectrum of BPM services in finance and accounting, procurement, customer interaction services and human resources leveraging collaborative models that are tailored to address the unique business challenges of each client. We co-create and execute the future vision of 400+ clients with the help of our 66,000+ employees.
Purpose of the role
To ensure AI and Copilot solutions are safe, reliable and compliant, covering both traditional QA and AI‑specific risks (bias, hallucination, explainability). The role defines assurance methods, quality gates and post‑deployment monitoring to meet internal policy and regulator expectations.
Key Accountabilities
Role Specific Accountabilities
- Design and manage the enterprise testing strategy for AI/Copilot, blending traditional QA with AI‑specific methods.
- Define test approaches for functional, performance, accuracy, reliability, ethical compliance and bias detection.
- Validate explainability, traceability and safety controls against policy and regulatory requirements.
- Evaluate and test human‑in‑the‑loop 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, low‑quality, domain‑specific and edge‑case inputs, to rigorously challenge model robustness and identify systemic weaknesses.
- Provide AI test engineering support to delivery squads, advising on model‑readiness criteria, testability risks, and quality implications of design decisions, ensuring solutions are verifiable throughout the lifecycle.
- Define and run post‑deployment validation, drift detection, incident triage and continuous model‑monitoring.
- 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 deep‑dive root‑cause analysis across data quality, model logic, prompt flows, integration layers and human‑in‑the‑loop steps; identify systemic failure points, recommend corrective actions, and drive end‑to‑end 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 hands‑on support.
- Champion continuous improvement of AI assurance, evaluating new testing tooling (LLM‑monitoring, bias‑scanners, prompt‑diff 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.
Shared Accountabilities
- Translate Divisional priorities into plans and deliverables to deliver overall Group strategic priorities
- Build the capability & capacity of functional resources to drive sustained commercial success
- Interpret & communicate the priorities for the Function, motivating and developing a high performing team
- Own functional priorities, applying specialist expertise to put the customer at the heart of everything and drive a profitable business
- Initiate and develop critical external and internal relationships which create value, collaborating to deliver commercial and customer priorities
- Uphold corporate legal & regulatory responsibilities
- Implement and manage transformation activity & harness innovation to create a high performing & sustainable business
Functional/Technical (Role Specific)
- 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 data‑driven systems, including multi‑component architectures, API‑integrated platforms, event‑driven workflows and systems operating under regulatory or high‑assurance constraints.
- Clear understanding of AI‑specific 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.
- 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 AI‑specific assurance, mapping out test plans, risk‑based prioritisation, acceptance criteria, model‑readiness thresholds and quality gates aligned to lifecycle stages.
- Ability to define and execute test plans across functional, non‑functional, ethical and performance dimensions, validating accuracy, latency, robustness, security, fairness, reliability and user‑journey consistency.
- Strong analytical mindset with the ability to identify root causes of defects or unexpected AI behaviour, performing deep‑dive diagnostics across data pipelines, vector stores, prompt flows, orchestration logic and human‑in‑the‑loop checkpoints.
- Experience with post‑deployment monitoring, drift detection and continuous validation, designing alerts, retraining triggers, performance thresholds and evaluation cadences to maintain long‑term model integrity.
- Comfortable learning and adapting to emerging AI technologies and engineering patterns.
- Excellent stakeholder management and communication skills, including senior‑level engagement.
- Commercial awareness and a value‑driven mindset.
Use of professional networks and external influencers with clear evidence of learning and development to build and maintain skills and expertise
- 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
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