Senior Test Engineer (UK) at CluePoints
Ontario, Ontario, Canada -
Full Time


Start Date

Immediate

Expiry Date

11 Dec, 26

Salary

0.0

Posted On

12 Sep, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Industry

Software Development & Publishing

Description

We’re proud to be an ambitious, fast-growing technology scale-up with a dynamic and diverse international team representing more than 20 nationalities. Collaboration, flexibility, and continuous learning are part of our DNA. 

At CluePoints, you’ll find a culture where you can grow, make an impact, and have fun along the way. Guided by our values of Care, Passion, and Smart Disruption, we’re united by a shared mission: to create smarter ways to run efficient clinical trials and deliver AI-powered insights that improve human outcomes worldwide.


The Role

We're hiring a Senior Test Engineer to help shape the next generation of AI-first quality engineeringat CluePoints. This is not a scripts-and-tickets role — you'll architect Playwright automation frameworks, build reusable AI skills and agents that accelerate the whole QE function, and set thestandards for how our engineers responsibly adopt AI in their day-to-day. You'll partner with squads across the engineering org, mentor testers and developers, and move theteam from simply using AI coding assistants to building governed, scalable AI-enabled qualityengineering capabilities.


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.

Responsibilities
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