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AI Test Lead; AI Foundry - from Office

Job in Leeds, West Yorkshire, ME17, England, UK
Listing for: WNS
Full Time position
Listed on 2026-04-17
Job specializations:
  • IT/Tech
    Data Security, Data Analyst
Salary/Wage Range or Industry Benchmark: 100000 - 125000 GBP Yearly GBP 100000.00 125000.00 YEAR
Job Description & How to Apply Below
Position: AI Test Lead (AI Foundry) - 3 Days Work from Office

Company Description

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.

Job Description

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.
  • Establish model‑evaluation techniques (prompt variability, edge‑case simulation, output consistency, scenario reasoning).
  • 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, ISO
    42001) 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…
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