QA Automation Architect
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Role details
Tech stack
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Job description
Prepare Test Plan, Test Strategy, reports like Test execution report, Release Readiness Report, Prod Defects RCA Reports Create and maintain reusable automation assets and documentation Analyze business requirements and prepare detailed test scenarios, test cases, and test data. Perform Functional, System Integration, Regression, Smoke, and support UAT testing. Design, develop, execute, and maintain automation test scripts using Selenium, Playwright, or UiPath. API Testing using Postman, REST Assured, SOAP UI or similar tools. Use AI tools to accelerate test case design, test data generation, automation script development, and test documentation creation Apply AI-driven techniques for defect triaging, root cause analysis, and impact assessment. Validate AI-generated test artifacts to ensure accuracy, completeness, and alignment with business requirements. Contribute to the development of AI-enabled testing best practices and quality engineering standards within the team. Collaborate with development teams to leverage AI solutions for improving SDLC and QA processes. Perform backend data validation and reconciliation using SQL queries. Identify, log, track, and validate defects throughout the defect lifecycle. Collaborate with Business Analysts, Developers, Product Owners, and stakeholders to ensure quality deliverables. Participate in Agile ceremonies and contribute to sprint planning and quality assurance activities. Support release validation, production testing, and root cause analysis activities. Continuously improving testing efficiency through automation and AI-assisted testing practices.
Requirements
Must Have Technical/Functional Skills Hands-on experience with at least one of the following: o Playwright o Selenium WebDriver o UiPath Test Suite Experience in automation framework design and maintenance. Knowledge of CI/CD integration and automated test execution Continuously evaluate and adopt emerging AI capabilities to optimize testing processes and increase automation coverage. Utilize AI-assisted code analysis to identify test scenarios, edge cases, and potential quality risks.
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