QA Engineer (SDET)

As a highly skilled Tooling and AI Automation Engineer (SDET-2) joining the Automation team, your role will be specialized in building internal tools, test intelligence platforms, dashboards, and AI-driven test enablement frameworks. You will thrive at the intersection of testing, data engineering, and internal tool development, empowering QA and Dev teams with intelligent systems and visual insights. Key Responsibilities: - Build and maintain robust, scalable test automation frameworks for API, web, and mobile platforms - Design, develop, and maintain internal tools to enhance automation capabilities, CI/CD integration, and test data visualization - Create intelligent dashboards for real-time reporting of test results, release quality metrics, and defect analytics - Develop AI/ML-based solutions to optimize test case prioritization, test suite reduction, flakiness prediction, and self-healing scripts - Integrate tools and dashboards with CI pipelines (e.g., Jenkins, GitHub Actions) and test frameworks (Selenium, Playwright, etc.) - Collaborate closely with SREs and DevOps teams to ensure scalable, performant, and well-adopted tools - Build APIs, microservices, or CLI tools supporting testing workflows, artifact management, and defect traceability - Continuously evaluate new AI/ML advancements and incorporate them into test engineering practices Required Skills: - Proficiency in Java and scripting languages - Hands-on experience in UI Testing (Java + Selenium), API Testing (Rest Assured), and Mobile Automation Testing (Appium - good to have) - Working knowledge or experience with Mabl, TestRigor, or any AI automation tool - Strong understanding of test automation frameworks (e.g., Pytest, Playwright, Cypress, Selenium) - Experience integrating tools with CI/CD pipelines (Jenkins, GitHub Actions, GitLab CI) - Familiarity with databases (SQL, NoSQL) and working with test data at scale - Experience in building REST APIs or microservices for internal use Nice to Have: - Experience with GenAI or LLM integration for test case generation or failure analysis - Working knowledge of AI/ML libraries like Scikit-learn, TensorFlow, or transformers for testing-centric use cases No additional details of the company were provided in the job description.,