QA / Automation Engineer (Proptech)

Node.js backend / Angular web / React Native mobile (post-migration)

AED 12,500 + performance-based bonus

OWNRSCLB is a trust-critical platform.

A bug in verification or form signing is not a UX issue; it is a business and legal issue.

We are looking for a QA/Automation Engineer who uses AI as a core part of their testing toolkit; not as a gimmick or buzzword, but as a genuine force multiplier.

You will use LLM-assisted tools to generate test cases, identify edge cases from requirements, accelerate coverage on a low-coverage codebase, and reduce the manual overhead of regression testing.

Our current test coverage is approximately 5%.

Your mandate is to change that faster than a traditional QA engineer could.

AI tooling is how you achieve it.

鈥⒙犅犅犅犅犅犅燶n**AI-assisted test generation**

Use LLM tools (GitHub Copilot, Claude, Cursor, or equivalent) to generate test suites from API specs, requirements documents, and codebase analysis.

You do not write every test from scratch; you direct AI to do the heavy lifting and validate the output.

Your bonus will be based on how effectively you can do this.

鈥⒙犅犅犅犅犅犅燶n**Critical path coverage**

Identify and prioritise the trust-critical flows; authentication, property verification, tier assignment, Form A/B signing, transaction workflows; and achieve meaningful automated coverage within the first 60 days.

鈥⒙犅犅犅犅犅犅燶n**Intelligent edge case discovery**

Use AI tools to surface edge cases and boundary conditions that manual analysis would miss.

Apply generative techniques to explore unexpected input combinations, race conditions, and failure modes.

鈥⒙犅犅犅犅犅犅燶n**API test automation**

Build and maintain automated test suites for the Node.js/Express backend API using AI-assisted tooling alongside Jest, Supertest, or Playwright.

鈥⒙犅犅犅犅犅犅燶n**End-to-end testing**

Implement E2E coverage for the web panels (Angular) and mobile app.

Use AI-powered tools (e.g.

Playwright with Copilot assistance, Testim, or Mabl) to accelerate test authoring.

鈥⒙犅犅犅犅犅犅燶n**AI-driven regression**

Establish a regression suite that runs on every pull request.

Explore AI-powered visual regression and self-healing test approaches to reduce maintenance overhead as the UI evolves.

鈥⒙犅犅犅犅犅犅燶n**Natural language test specs**

Work with the Business Analysts to translate requirements written in plain English directly into executable test cases using LLM-based test generation tools.

鈥⒙犅犅犅犅犅犅燶n**Security testing**

Use AI-assisted tools (e.g.

OWASP ZAP with AI extensions, or LLM-guided penetration prompts) to complement manual security testing on authentication, input validation, and access control.

鈥⒙犅犅犅犅犅犅燶n**QA process ownership**

Define what must pass before a PR merges, own the definition of done, and provide sign-off before every release.

Document your AI toolchain so the team can build on it.

鈥⒙犅犅犅犅犅犅燶n**Active AI tooling user**

you currently use GitHub Copilot, Claude, Cursor, or similar LLM tools as part of your daily workflow; not occasionally, but habitually.

You can demonstrate this in your interview.

鈥⒙犅犅犅犅犅犅燶n**AI test generation**

hands-on experience generating test cases from specs or code using LLM tools.

You understand where AI output needs validation and where to trust it.

鈥⒙犅犅犅犅犅犅燶n**3+ years**

of QA engineering experience with demonstrated automation skills; not just manual testing.

鈥⒙犅犅犅犅犅犅燶n**API testing**

strong experience writing automated tests against REST APIs.

You are comfortable with JSON, HTTP, auth headers, and testing error conditions.

鈥⒙犅犅犅犅犅犅燶n**JavaScript / TypeScript**

sufficient programming ability to direct, review, and refine AI-generated test code.

You can tell when Copilot has written something wrong.

鈥⒙犅犅犅犅犅犅燶n**Test frameworks**

hands-on experience with Jest, Supertest, Playwright, Cypress, or equivalent.

You use these alongside AI tools, not instead of them.

鈥⒙犅犅犅犅犅犅燶n**CI/CD integration**

experience configuring tests to run automatically in a GitHub Actions or GitLab CI pipeline.

Nice to Have:

鈥⒙犅犅犅犅犅犅燶n**AI testing platforms**

experience with dedicated AI testing tools such as Testim, Mabl, Applitools, or Reflect; platforms that use AI for test creation, self-healing, and visual regression.

鈥⒙犅犅犅犅犅犅燶n**Prompt engineering for QA**

a working understanding of how to prompt LLMs effectively to generate high-quality, relevant test cases rather than generic boilerplate.

鈥⒙犅犅犅犅犅犅燶n**Mobile testing**

experience with Detox, Appium, or AI-assisted mobile testing tools.

鈥⒙犅犅犅犅犅犅燶n**Performance testing**

AI-assisted load testing with k6 + Copilot, Artillery, or similar.

鈥⒙犅犅犅犅犅犅燶n**Security testing**

OWASP awareness; experience using AI tools to surface injection, auth, and access control vulnerabilities.

鈥⒙犅犅犅犅犅犅燶n**Real estate / PropTech**

understanding of property transaction workflows or similar regulated processes.

鈥⒙犅犅犅犅犅犅燶n**Stage 1**

45-minute screen leadership; walk us through how you currently use AI in your testing workflow.

Be specific: which tools, what prompts, what results.

We will ask follow-up questions.

鈥⒙犅犅犅犅犅犅燶n**Stage 2**

Take-home paid task (2 hours); given our API spec and a set of plain-English requirements, produce a test suite using whatever AI tooling you choose.

Submit both the tests and a brief explanation of how you used AI to generate them.

We assess quality of output and intelligence of approach.

鈥⒙犅犅犅犅犅犅燶n**Stage 3**

Live paid session (1 hour); screen-share a live demonstration of your AI-assisted testing workflow against a simple API endpoint we provide.

We want to see how you actually work, not how you describe working.

鈥⒙犅犅犅犅犅犅燶n**Stage 4**

Final conversation; team fit, toolchain discussion, compensation.