Job requirements

What You’ll Bring

• Extensive hands-on experience architecting scalable Playwright automation frameworks for enterprise-grade web applications — maintainability, reusability, low-flake design.
• Expert-level TypeScript / JavaScript with modern software engineering principles, design patterns, and clean-code discipline.
• Demonstrated use of AI coding assistants (GitHub Copilot, Codex, Claude, Cursor, ChatGPT, or similar) in real delivery — with governance and validation, not just consumption.
• Proven experience designing AI-native quality engineering solutions: prompt engineering, AI-assisted test generation, intelligent test maintenance, self-healing, and evaluation of AI outputs.
• Hands-on experience building reusable AI skills / agents for testing use cases — test generation, requirements analysis, failure analysis, exploratory testing, release-quality assessment.
• Strong integration of automation into CI/CD, containerised test execution (Docker, K8s / K3s), and cloud-native test infrastructure.
• Deep grounding in software quality engineering — risk-based testing, test design techniques, exploratory testing, shift-left, and defect prevention.
• Ability to mentor and technically lead — architecture reviews, coaching, standards, and driving AI adoption across squads.
• Strong stakeholder communication — able to influence engineering teams and leadership on automation strategy, AI adoption, and QE transformation.

Job responsibilities

What You’ll Be Doing

• Architect and evolve scalable Playwright automation frameworks in TypeScript / JavaScript — modular design, POM / Screenplay, API abstraction layers, reusable utilities, and low-flake stability.
• Design and build AI-native testing capabilities: reusable AI skills, agents, and workflows for test generation, intelligent maintenance, self-healing, failure analysis, and release-quality assessment.
• Drive responsible AI adoption across squads — establish standards for when AI-generated code and tests can be trusted, reviewed, modified, or rejected.
• Integrate automation into modern CI/CD pipelines with parallel, containerised execution (Docker, Kubernetes / K3s) and quality gates that hold the line without slowing delivery.
• Own automation strategy across API, UI, integration, and end-to-end layers — risk-based, aligned to the right level of the test pyramid.
• Optimise AI workflows for cost and quality: prompt engineering, context management, token/model selection, reuse of skills and agents, and safe autonomy limits.
• Mentor engineers, run architecture and code reviews, and measure the impact of AI-driven testing with meaningful metrics — productivity, coverage, defect detection, maintenance effort, AI accuracy, cost.
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