QA / Automation Engineer (Proptech)
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Key skills for this role
About the Role
OWNRSCLB is seeking a QA/Automation Engineer to drive test coverage using AI-assisted tools. The role focuses on critical path automation, API testing, and end-to-end testing for a proptech platform.
Key Skills for This Role
Responsibilities
- Use LLM tools to generate test suites from API specs, requirements, and codebase analysis
- Identify and prioritize trust critical flows for automated coverage within first 60 days
- Use AI tools to surface edge cases and boundary conditions
- Build and maintain automated test suites for Node.js/Express backend API
- Implement E2E coverage for web panels (Angular) and mobile app
- Establish a regression suite that runs on every pull request
- Translate plain English requirements into executable test cases using LLM based tools
- Use AI assisted tools for security testing
- Define QA process, own definition of done, and provide sign off before releases
Requirements
- Active AI tooling user (GitHub Copilot, Claude, Cursor, or similar)
- AI test generation experience
- 3+ years QA engineering experience with automation
- API testing experience (REST APIs)
- JavaScript/TypeScript programming ability
- Experience with Jest, Supertest, Playwright, Cypress, or equivalent
- CI/CD integration experience (GitHub Actions or GitLab CI)
Full Job Posting
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.
- Our current test coverage is approximately 5%. Your mandate is to change that faster than a traditional QA engineer could.
Key Responsibilities
- AI assisted test generation: Use LLM tools to generate test suites from API specs, requirements documents, and codebase analysis.
- Critical path coverage: Identify and prioritise trust critical flows and achieve meaningful automated coverage within the first 60 days.
- Intelligent edge case discovery: Use AI tools to surface edge cases and boundary conditions.
- 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 using AI powered tools.
- AI driven regression: Establish a regression suite that runs on every pull request.
- Natural language test specs: Work with Business Analysts to translate plain English requirements into executable test cases.
- Security testing: Use AI assisted tools to complement manual security testing.
- QA process ownership: Define what must pass before a PR merges, own the definition of done, and provide sign off before every release.
Must Have
- Active AI tooling user: you currently use GitHub Copilot, Claude, Cursor, or similar LLM tools as part of your daily workflow.
- AI test generation: hands on experience generating test cases from specs or code using LLM tools.
- 3+ years of QA engineering experience with demonstrated automation skills.
- API testing: strong experience writing automated tests against REST APIs.
- JavaScript / TypeScript: sufficient programming ability to direct, review, and refine AI generated test code.
- Test frameworks: hands on experience with Jest, Supertest, Playwright, Cypress, or equivalent.
- CI/CD integration: experience configuring tests to run automatically in a GitHub Actions or GitLab CI pipeline.
Nice to Have
- AI testing platforms: Testim, Mabl, Applitools, or Reflect.
- Prompt engineering for QA.
- Mobile testing: 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 vulnerabilities.
- Real estate / PropTech understanding.
Interview Process
- Stage 1: 45 minute screen leadership; walk through how you currently use AI in your testing workflow.
- Stage 2: Take home paid task (2 hours); produce a test suite using AI tooling.
- Stage 3: Live paid session (1 hour); screen share a live demonstration of your AI assisted testing workflow.
- Stage 4: Final conversation; team fit, toolchain discussion, compensation.
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