See what you can do with Prophix 

Prophix helps finance teams streamline planning, reporting, and analysis through Prophix One™, our Financial Performance Platform. We bring automation, collaboration, and clarity together so people can focus on work that has impact. As we expand our AI-enabled capabilities, you will join a team where intelligent tools support meaningful contributions and people guide each decision with intention. 

We have teams and offices across the UK, Europe, North America, and Australia.  

The QA Lead drives improvements in quality, productivity, and cost efficiency through AI-enabled QA practices and optimized processes, ensuring the timely delivery of scalable, high-quality software. As a hands-on technical leader, you champion AI adoption, enforce best practices, and elevate QA performance through influence rather than direct management. 

What You Will Do 

• Participate in defining and enforcing QA best practices across contributors 

• Promote cost-aware, value-driven quality engineering 

• Identify systemic quality issues and lead improvements in workflows, tooling, execution quality, and team capabilities 

• Act as a technical authority for QA in the design and evolution of AI-assisted test strategies guiding both manual and automated testing approaches 

• Champion AI-first QA practices, applying AI tools to address common QA needs and improve reusability of these with practical approaches for consistent velocity and quality improvements across contributors 

• Perform manual testing using AI-assisted techniques (white-box and black-box) 

• Design and implementing automated tests (e.g. Jest, Playwright) 

• Use prescribed AI tools (e.g. Claude Code) to accelerate QA activities 

• Review and continuously improve the quality of team outputs (stories, defect reports, test cases, automation, etc.) 

• Provide hands-on training and coaching to team members on AI tools, test design, and automation 

• Drive adoption of best practices in prompt design and context engineering 

• Stay current with AI trends and introduce improvements 

• Focus effort on protecting high-risk, business-critical workflows, while making intentional trade-offs in prioritising QA activities to balance critical versus non-essential product functionality, scope of regression risk beyond immediate changes, time and infrastructure cost required to validate