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AI QA Role Requirements — AI Test Engineer & AI QA Lead

What this is: a generalised, company-agnostic checklist of what employers ask for in two AI-quality role families — AI Test Engineer (hands-on individual contributor) and AI QA Lead (strategy + people + governance). Use it to self-assess, target learning, and tailor a CV.

Note: this is a synthesis of typical UK-market requirements for these roles, not a copy of any single advert — no company names, salaries, or locations. Cross-reference with the QA → AI QA transition plan.

How the Two Roles Differ

   AI Test Engineer  ───────────────────▶  AI QA Lead
   (do the testing)                        (own the quality outcome + people)

   hands-on execution        adds ▶  strategy · team leadership · governance ·
   frameworks · automation            stakeholder management · roadmap ownership
Dimension AI Test Engineer AI QA Lead
Level Mid → Senior IC Senior / Lead / Manager
Primary value Builds and runs tests Owns quality strategy & the team
Reports to Test/QA Lead or Eng Manager Quality Director / Head of Engineering
Team None (peer collaboration) 3–7 testers across squads
Focus verbs build, automate, execute, validate define, lead, mentor, govern, report

Group A — AI Test Engineer / AI Automation Tester

Core responsibilities

  • Design, build, and maintain automated test frameworks for web, API, and data layers.
  • Write and execute functional, regression, integration, and end-to-end tests.
  • Test AI/LLM-powered features — validate non-deterministic outputs, not just fixed assertions.
  • Build and run evaluation suites for LLM/RAG systems (accuracy, hallucination, relevance).
  • Integrate automated tests into CI/CD pipelines and maintain green builds.
  • Investigate failures, raise well-documented defects, and support root-cause analysis.
  • Contribute to test planning, coverage analysis, and test data management.
  • Collaborate with developers, product, and data teams in Agile ceremonies.

Must-have skills & experience

  • Strong test automation experience (typically 3+ years hands-on).
  • A programming language for test code — Python, JavaScript/TypeScript, Java, or C#.
  • UI automation with Playwright, Selenium, or Cypress.
  • API testing — Postman, REST Assured, or code-based HTTP testing.
  • Solid grasp of CI/CD (GitHub Actions, Jenkins, Azure DevOps, GitLab CI).
  • Version control with Git.
  • Understanding of testing types: functional, regression, integration, performance, accessibility.
  • Working knowledge of SQL and basic data validation.
  • Familiarity with Agile/Scrum delivery.

AI-specific skills (the differentiator)

  • Understanding of LLMs, RAG, embeddings, and prompts at a working level.
  • Experience or familiarity with LLM evaluation frameworks — RAGAS, DeepEval, promptfoo.
  • LLM-as-a-judge and golden-dataset evaluation concepts.
  • Awareness of prompt injection / adversarial testing and AI safety.
  • Use of AI-assisted testing tools — GitHub Copilot, Cursor, AI test generation.

Common tech stack

Languages   Python · TypeScript/JavaScript · Java · C#
UI          Playwright · Selenium · Cypress
API         Postman · REST Assured · pytest/requests
AI eval     RAGAS · DeepEval · promptfoo · LangChain/LlamaIndex (awareness)
CI/CD       GitHub Actions · Jenkins · Azure DevOps · GitLab
Data        SQL · pandas · basic ETL validation
Cloud       AWS / Azure / GCP (awareness of one)
Tooling     Git · Jira · Docker

Nice-to-have

  • Cloud AI service exposure (AWS Bedrock, Azure OpenAI, Vertex AI).
  • Performance testing (JMeter, k6, Gatling).
  • Contract testing (Pact), BDD (Cucumber/SpecFlow).
  • Observability tooling (Langfuse, OpenTelemetry).
  • ISTQB Foundation certification.

Group B — AI QA Lead

Everything in Group A, plus:

Core responsibilities

  • Own the end-to-end quality strategy for one or more product areas / squads.
  • Lead, mentor, and grow a team of test engineers (typically 3–7).
  • Define and champion an AI-first testing approach across teams.
  • Establish quality metrics, KPIs, and reporting for leadership.
  • Own risk-based test strategy and release-readiness / go-no-go decisions.
  • Set standards and governance — test frameworks, coverage bars, definition-of-done.
  • Manage stakeholder relationships across product, engineering, and delivery.
  • Drive test automation strategy and continuous-improvement of the QA function.
  • Ensure compliance where relevant (data privacy, regulated domains, audit trails).
  • Contribute hands-on to complex automation and evaluation work when needed.

Must-have skills & experience

  • Proven QA/test experience at senior level (often 5–8+ years) with leadership.
  • Track record managing or mentoring test engineers.
  • Deep test strategy and risk-based testing expertise.
  • Strong stakeholder management and communication skills.
  • Experience embedding QA in Agile delivery at scale (multiple squads).
  • Hands-on automation background (so the strategy is credible).
  • Experience defining quality metrics and reporting to management.

AI leadership skills (the differentiator)

  • Ability to define an AI testing strategy — LLM/RAG/agent evaluation at programme level.
  • Champion AI-assisted QE adoption across a team (tooling, process, upskilling).
  • Understanding of AI governance & risk — safety, bias, data privacy, model drift.
  • Ability to build continuous evaluation pipelines and quality gates for AI features.

Frequently listed for Lead roles

  • Security clearance (e.g. SC clearance) for government/defence/regulated roles — often a hard requirement; may require existing eligibility.
  • Regulated-domain experience (finance, healthcare, government, clinical).
  • Budget / vendor / tooling ownership.
  • Recruitment and capability-building responsibility.

Shared Baseline (Both Roles)

Category Expectation
Mindset Quality advocacy, attention to detail, curiosity about how things fail
Non-determinism Comfort testing systems where the same input yields different valid outputs
Communication Clear defect writing, test reporting, cross-team collaboration
Delivery Agile/Scrum, CI/CD, shift-left testing
Foundations Git, SQL, one programming language, one automation framework
AI literacy LLMs, RAG, evaluation metrics, prompt/adversarial awareness
Continuous learning The field moves fast — evidence of self-directed upskilling

CV / ATS Keyword Bank

Drop the relevant terms into a CV so applicant-tracking systems match you:

Test Automation · Playwright · Selenium · Cypress · TypeScript · Python · Java · C#
API Testing · Postman · REST Assured · CI/CD · GitHub Actions · Jenkins · Azure DevOps
Git · SQL · Agile · Scrum · Regression Testing · Integration Testing · E2E Testing
LLM Testing · RAG Evaluation · RAGAS · DeepEval · promptfoo · LLM-as-a-Judge
Hallucination · Prompt Injection · AI Safety · Non-Deterministic Testing · Golden Dataset
Quality Strategy · Risk-Based Testing · Test Leadership · Mentoring · Quality Metrics
Release Readiness · Stakeholder Management · AI-First QA · GitHub Copilot · Cursor
AWS Bedrock · Azure OpenAI · Vertex AI · Langfuse · Observability · SC Clearance · ISTQB

How to Use This Page

  1. Self-assess — tick what you have; the gaps are your learning backlog.
  2. Target learning — feed gaps into the 6-Week Transition Plan.
  3. Tailor your CV — mirror the exact keywords from a specific advert (ATS matching).
  4. Prep interviews — every requirement is a likely question; see AI Test Strategy for answers.

Where to Go Next