QA / Automation Engineer (Proptech)

Location: On Site (Dubai)

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

Salary: AED 12,500 + performance-based bonus

About the Role

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.

Key Responsibilities:

•        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.

•        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.

•        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.

•        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.

•        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.

•        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.

•        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.

•        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.

•        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.

Must Have:

•        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.

•        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.

•        3+ years of QA engineering experience with demonstrated automation skills; not just manual testing.

•        API testing strong experience writing automated tests against REST APIs. You are comfortable with JSON, HTTP, auth headers, and testing error conditions.

•        JavaScript / TypeScript sufficient programming ability to direct, review, and refine AI-generated test code. You can tell when Copilot has written something wrong.

•        Test frameworks hands-on experience with Jest, Supertest, Playwright, Cypress, or equivalent. You use these alongside AI tools, not instead of them.

•        CI/CD integration experience configuring tests to run automatically in a GitHub Actions or GitLab CI pipeline.

Nice to Have:

•        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.

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

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

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

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

•        Real estate / PropTech understanding of property transaction workflows or similar regulated processes.

Interview Process:

•        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.

•        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.

•        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.

•        Stage 4 Final conversation; team fit, toolchain discussion, compensation.