Knowledge AI Quality Lead

We are seeking a detail-oriented Knowledge AI QA Lead to monitor, test, and evaluate our AI-driven customer experiences to ensure effortless, accurate, and scalable support for all our customers. This role focuses on the end-to-end quality of the customer support journey - from Self-Service and AI-Assist through to ...

This role is a hybrid position that requires 1-2 days per week in our London or Oxford office.

Quality Framework & Testing: Design and execute QA frameworks, test scripts, and evaluation criteria specifically for AI chatbots, automated workflows, and agent copilot tools.

Quality Framework & Testing: Design and execute QA frameworks, test scripts, and evaluation criteria specifically for AI chatbots, automated workflows, and agent copilot tools.
Performance Monitoring: Continuously audit and analyze real-time AI conversations and self-service flows to identify intent misclassifications, hallucinations, inaccurate answers, or broken customer journeys.
Closed-Loop Quality Feedback: Identify root causes of AI accuracy failures and customer friction; feed actionable insights directly back to AI Automation Leads, Product, and Engineering teams for sprint training and fine-tuning.
Human-Assist QA Integration: Audit the handover from AI to Human-Assist to ensure context retention, CRM integration accuracy, and seamless agent workflow execution.
Benchmarking & Accuracy Scoring: Track and report on key QA metrics, including intent recognition accuracy, resolution precision, containment effectiveness, and compliance standards.
Data Validation & Testing: Conduct pre-release user acceptance testing (UAT) and regression testing for all new AI prompt deployments, KB updates, and workflow releases.
Defect & Gap Analysis: Partner with operational teams to identify recurring knowledge base gaps and out-of-scope customer queries to broaden AI coverage safely.

 

 
Job Location: Hybrid
 
QA or Process Improvement specialist with 2+ years of experience in customer contact-center operations or tech support environments.
Hands-on experience in testing or auditing AI conversational flows, chatbots, virtual assistants, or automated workflow tooling.
Familiarity with process improvement methodologies (e.g., Lean, Six Sigma) applied to quality control and operational auditing.
Strong understanding of AI behavior, intent structures, and natural language understanding (NLU) failure modes.
Proven ability to translate operational customer friction into technical feedback for engineering and product teams.
Demonstrated experience in data logging, audit reporting, and root-cause defect analysis.
Customer-obsessed mindset with meticulous attention to detail and standard-setting for response quality.
Communication: Clear written and verbal reporting skills with the ability to articulate quality defects to technical and non-technical stakeholders (required).
Analytical Mindset: Exceptional eye for detail with the ability to spot subtle pattern failures in data and conversational logs (required).
Data Driven: Comfortable leveraging call/chat logs, accuracy metrics, and A/B test results to validate performance (required).
Industry: Experience in a similar role within the Travel Industry (Preferred).
Impact-Driven: Motivated by delivering high Traveler Sentiment Scores (TSS) through effortless resolution, protecting brand trust, and ensuring travelers enjoy exceptional experiences.