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Metro location and common QA automation skills increase competition, while AI-testing specialization slightly reduces it.
QA automation skills transfer across industries, but AI/ML validation increases domain-specific bias.
Multiple mandatory automation and AI-testing skills imply moderate filtering without strict numeric experience requirements.
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Design, develop, and maintain scalable automation frameworks for web, API, backend, and data-driven applications.
Validate AI/ML model APIs, inference services, ML pipelines, and AI-enabled features including LLM-based systems with focus on prompt validation and output consistency.
Integrate automated test suites into CI/CD pipelines and collaborate cross-functionally to define quality strategy and manage defect lifecycle.
Proficiency in automation scripting languages such as Java, Python, JavaScript, or TypeScript.
Experience with UI automation tools (e.g., Selenium WebDriver, Playwright, Cypress) and API testing tools (e.g., Postman, RestAssured, Karate DSL).
Familiarity with BDD frameworks like Cucumber, SpecFlow, or Behave and CI/CD integration tools such as Jenkins, GitHub Actions, or Azure DevOps.
Work Experience Required: Not explicitly mentioned in the JD; Location: Bengaluru; Working Model: Work From Office.
Strong expertise in QA automation focused on AI/ML systems, including advanced validation techniques for AI-driven features and datasets.
Experienced in integrating automation frameworks within CI/CD pipelines and collaborating closely with cross-functional teams including developers, data scientists, and DevOps.
Prior exposure to testing manufacturing, semiconductor, industrial automation, IoT, or data-intensive systems is preferred.