The role

Kaluza has new opportunities for QA Developer Engineers who will build and maintain automation frameworks that test AI-generated and AI-assisted code, and develop agent-based tooling that supports engineering teams across the business. These roles sit at the intersection of software development, quality engineering, and applied AI — you will build the tools that other engineers and clients rely on to develop, test, and ship AI-driven solutions with confidence.

The QA Development Engineers will be part of a Quality Enablement function which forms part of Kaluza’s Quality departments.

They will work closely with Applied Engineering and Global Products teams to deliver reusable frameworks and agent solutions. You will have opportunities to work with internal and external clients to understand their requirements and build solutions to meet their needs. AI will be a core part of your own workflow — used to accelerate

the design, build, and iteration of testing tools and frameworks.

What you will be doing

• Automation Framework Development: build and maintain automation frameworks specifically for testing AI-generated code and AI system outputs, ensuring accuracy, reliability, and consistency.
• Agent Solution Development: Create agent-based tools and solutions that Applied Engineering and Global Products teams can use to deliver work faster, and that developers can use to test their code and systems.
• AI-Assisted Tool Building: Use AI tools and techniques to build, refine, and scale internal testing tools and frameworks efficiently, embedding AI into your own development lifecycle.
• Solution-Driven Delivery: Operate as part of a solution-focused team, translating customer and stakeholder requirements into working tools, frameworks, and processes.
• Continuous Improvement: collaborate with other team members to identify bottlenecks in code and architecture, and build internal AI-driven tools to accelerate our test-and-deploy lifecycles. participate in constructive code reviews to champion best engineering practices and maintain high code quality.
• Cross-Team Collaboration: Partner with internal engineering teams (as internal customers) to understand their AI deployment constraints and testing needs — gathering requirements and delivering quality frameworks and solutions that meet real needs.
• Documentation and Enablement: Produce documentation and guidance so that developers, QA teams, and clients can adopt and use the frameworks and agent tools effectively.
• Feature Delivery and Ownership: Take technical ownership of discrete components and features end-to-end, writing clean, well-tested code
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