Senior Quality Assurance Automation Engineer

1 week ago

Bengaluru, Karnataka, India Landmark Group Full time ₹ 20,00,000 - ₹ 25,00,000 per year

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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