Senior Quality Assurance Automation Engineer

4 days ago

Bengaluru Karnataka India, Karnataka Landmark Group Full time

About the Role- We are looking for a highly skilled Tooling and AI Automation Engineer with 5+ years of relevant experience to join our Automation team—not in a traditional application testing but as a specialist focused on building internal tools, test intelligence platforms, dashboards, and AI-driven test enablement frameworks.


Required Skills:

• Proficiency in Java/ Python.

• Experience in building REST APIs or microservices for internal use and building new testing frameworks from scratch.

• Strong understanding of test automation frameworks (e.g., Pytest, Playwright, Cypress, Selenium).

• Solid Hands on experience in API Testing using Rest Assured, Mobile Automation using Appium and basics of Performance testing.

• Working knowledge or experience with Mabl, TestRigor, or any AI automation tool.

• Experience with GenAI or LLM integration for test case generation or failure analysis.

• Experience integrating tools with CI/CD pipelines (Jenkins, GitHub Actions, GitLab CI).

• Familiarity with databases (SQL, NoSQL) and working with test data at scale.

• Good in Debugging, Test Case planning, problem solving and logical thinking.


Key Responsibilities:

• Test Automation Development: Build and maintain robust, scalable test automation frameworks for API, web, and mobile platforms.

• Design, develop, and maintain internal tools that enhance automation capabilities, CI/CD integration, and test data visualization.

• Build 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.).

• Work closely with SREs, and DevOps teams to ensure tools are scalable, performant, and well-adopted.

• Create APIs, microservices, or CLI tools that support testing workflows, artifact management, and defect traceability.

• Continuously evaluate new AI/ML advancements and incorporate them into test engineering practices

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