QA Lead AI-Driven Quality Engineering

Solutions21 builds web, mobile and AI-powered products for clients in healthcare, education and real estate, and builds its own AI product. You’ll own quality across several active client projects at once, often working within each client’s own repositories, toolchains and delivery processes. You’ll build a lean, AI-driven QA practice for the whole company that works whatever tools a project uses. We care more about judgement than tool names. Tell us what you’d choose and why.

Reports to:SVP – Technology Delivery

Role purpose

Own quality across several active client projects and build a lean, AI-driven QA practice for the whole company that works whatever tools a project uses — as a senior hands-on lead.

Key Responsibilities

• QA & automation strategy: Assess our current projects. Then define a practical, risk-based QA approach and roadmap for the company, with AI agents at its core. Decide what AI should own, what needs human judgement, and what not to automate at all. Keep governance light.
• Automation framework: Extend, maintain and improve our automation framework (e.g. Playwright with TypeScript) so it scales across UI, APIs, integrations and backend/database data checks. Make it easier for AI agents to generate, extend and maintain tests on it. Keep improving the reusable starter kit that developers run on their own projects.
• CI/CD & quality gates: Build AI-driven test runs and quality gates into whatever CI/CD platform a project uses (e.g. GitHub Actions, GitLab CI, Azure DevOps). Start from reusable templates and set up each client environment for its engagement. Feedback should be fast and reliable.
• AI-driven quality engineering: Set up AI agents that generate test cases and automation scripts from user stories and acceptance criteria; run and maintain test suites; do exploratory testing; and triage and analyse defects. Set the guardrails and review points that make their output trustworthy. Keep test authoring independent of the tools developers use to build.
• AI/LLM quality validation: Set up evaluation for features built on LLMs — groundedness and hallucination checks, RAG retrieval quality, prompt adherence, regression evals, and prompt-injection risk.
• Visual & resilient UI testing: Add AI-driven visual regression testing and self-healing locators to keep maintenance close to zero as the platform changes.
• Test management & release readiness: Own test cases, the defect process (severity and priority) and traceability in whatever tools a project uses. Report clearly on whether each release is ready.
• Team & partners: Lead and mentor a junior QA engineer. Coordinate partner and vendor testers. Coach developers on writing stories that AI can test well and on unit testing.
  
  

• 30 days: Existing framework, pipelines and projects assessed. Risk map and gap analysis done. Improvement roadmap agreed.
• 60 days: First measurable improvements delivered on active projects — wider coverage of critical user journeys, fewer flaky tests, faster pipeline feedback, and more reliable AI-generated tests. Evaluation approach for AI features defined.
• 90 days: Improved starter kit and quality gates adopted on all active projects, release-quality reporting in place, and an evaluation suite running for our AI features.
  
  

• 6+ years of hands-on quality engineering, including at least 2 years owning QA strategy and scaling automation frameworks across several projects.
• Hands-on experience running AI-driven testing: agents that generate, run and maintain tests. Experience only with chat-based assistance isn’t enough.
• Strong experience with a modern automation framework (Playwright preferred; Cypress or Selenium acceptable), in TypeScript/JavaScript or Python.
• Strong API and integration testing skills, and the SQL to verify backend data.
• Experience building automated suites and quality gates into CI/CD pipelines on more than one platform.
• Experience with test management and defect workflows in industry-standard tools, and the ability to adapt quickly to each client’s toolchain.
• Experience working across several projects or clients at once.
  
  

• Hands-on LLM evaluation (e.g. Promptfoo, DeepEval, Ragas): RAG validation, regression evals, prompt testing.
• AI-driven visual testing (e.g. Applitools, Percy).
• Mobile automation (e.g. Appium, Maestro, Detox).
• Regulated domains such as healthcare or government, including environments where AI tools aren’t permitted.
• Performance testing (e.g. k6) and basic security testing.