AI QA Engineer

Requirements:

• 6+ years of experience in Software QA / Testing / SDET.
• Strong experience in test automation rather than purely manual testing.
• Hands-on experience with tools such as Selenium, Playwright, Cypress, Appium, or similar automation frameworks.
• Strong programming/scripting experience in Python, Java, JavaScript, or TypeScript.
• Experience with API automation, functional testing, regression testing, integration testing, and end-to-end testing.
• Strong understanding of QA methodologies, test strategy, test planning, and defect management.
• Experience integrating automation frameworks with CI/CD pipelines.

AI / GenAI for QA:

• Minimum 2 years of hands-on experience applying AI/GenAI to software testing or QA automation.
• Experience with LLMs such as OpenAI, Claude, Gemini, Azure OpenAI, Llama, or similar models.
• Experience using AI for automated test-case/test-scenario generation.
• Experience using AI/LLMs to generate or enhance automation scripts.
• Experience with AI-powered test execution, test prioritisation, or regression optimisation.
• Experience using AI for test failure analysis, log analysis, defect analysis, or root-cause analysis.
• Understanding of AI agents / agentic workflows for software testing is highly desirable.
• Experience building or integrating AI-powered QA tools, frameworks, or internal solutions will be a strong advantage.

Good to Have:

• Experience with LangChain, LlamaIndex, RAG, AI agents, or LLM APIs.
• Experience with self-healing test automation.
• Experience building AI agents capable of independently executing testing workflows.
• Knowledge of LLM evaluation, prompt engineering, and AI testing methodologies.
• Experience with Docker, Kubernetes, Jenkins, GitHub Actions, or other DevOps tools.
• Experience working in Agile/Scrum environments.

Nice to Have:

The ideal candidate should be someone who has built or implemented AI-driven QA solutions, rather than someone who has only used ChatGPT or other AI tools for basic productivity.

We are particularly interested in candidates who can demonstrate how they have used AI to:

• Reduce test execution time
• Increase test coverage
• Automate test-case creation
• Generate automation scripts
• Reduce regression testing effort
• Analyze failures automatically
• Reduce flaky/maintenance-heavy tests
• Improve overall QA productivity and release velocity