AI Quality & Automation Engineer

In simple terms, you are the person who makes sure what we build actually works and builds systems to catch what could go wrong before it reaches production.
Responsibilities:
Own quality end-to-end: Take responsibility for product quality across development, testing, and deployment, not just individual test cases or automation suites.
Build the quality framework from 0-1: Design and build a strong, scalable testing environment based on our actual product, systems, workflows, and failure modes, not a generic testing template.
Drive testing best practices: Establish and continuously evolve best practices across test design, automation, coverage, validation, regression prevention, reliability, and release quality.
Continuously improve the testing practice: Stay curious about emerging testing approaches, AI capabilities, tools, and industry practices; identify what can make our quality process better and actively implement what is valuable.
Bring learning into the organisation: Experiment with new tools, techniques, and approaches, turn useful learnings into repeatable practices, and help the broader engineering team adopt them.
Build proactive quality systems: Think beyond testing what already exists. Identify how systems can fail, anticipate edge cases, and build checks that catch problems before they reach production.
AI-driven testing: Use AI throughout the testing lifecycle, from understanding changes and generating test scenarios to writing tests, debugging failures, identifying gaps, and validating releases.
Validate AI-generated development: Build mechanisms to independently validate code, features, and system changes produced with AI so that development speed does not come at the cost of reliability.
Build intelligent automation: Create automation that is reliable, adaptive, maintainable, and capable of evolving as the product and engineering practices change.
Own deployment quality: Integrate automated validation into development and deployment workflows so changes are continuously tested before and after release.
Find what others don't: Go beyond expected test scenarios, challenge assumptions, and identify edge cases, failure modes, and gaps that traditional testing approaches may miss.
Raise the engineering quality bar: Make quality a shared engineering value by partnering with engineering and product teams and influencing how quality is built into the development lifecycle.
Challenge existing approaches: Question how testing is normally done, identify better ways of working, and introduce approaches that improve speed, coverage, confidence, and reliability.
Influence without authority: Bring clarity, evidence, and practical solutions that help the broader engineering team adopt stronger quality practices.
Requirements:
4-8 years of experience in test automation, quality engineering, software testing, or a closely related engineering role.
Strong logical and analytical thinking with the ability to break down complex systems and problems.
Strong understanding of how software can fail and the ability to proactively identify edge cases and failure modes.
Hands-on experience building or owning automation frameworks end-to-end.
Deeply hands-on with AI development and engineering tools such as Claude Code, Grok, Codex, and similar tools.
Ability to use AI for test generation, code generation, debugging, analysis, refactoring, coverage improvement, and problem-solving.
Ability to understand and reason across different layers of a software system.
Understanding of deployment workflows, CI/CD, release processes, and production environments.
Ability to work across multiple frameworks, languages, and tools based on what the product requires.
Strong debugging skills and the ability to trace issues across multiple layers of a system.
Ability to design testing approaches based on the specific product and system rather than relying on predefined templates.
Curiosity to continuously learn new tools, technologies, and testing approaches, and the ability to turn useful learning into practical improvements.
Ability to influence engineers and teams through technical clarity, collaboration, and strong reasoning.
Strong ownership mindset with the ability to build a quality practice from the ground up.
Ability to balance speed, coverage, reliability, and maintainability in a fast-moving environment.
Tech Environment: What You'll Work With
Claude Code
Grok
Codex
AI-assisted development and testing tools
AI-powered automation
Emerging AI testing and quality tools
Automation frameworks and tools that best fit the product and problem

Skills
Automation Testing, API Testing, CI / CD, SDET, Claude, LLMs, MCP, Agentic AI

Experience
3-7 yrs