- Conducted Manual and Automation Testing for Inventory Smart Application, achieving a 20% reduction in post-production defects, ensuring seamless functionality for major retail clients like Dollar General,Carters, and Ralph Lauren.- Performed Regression Testing, Smoke Testing, and Load Testing to ensure system stability andperformance under high traffic and resource loads.- Contribute to Test Planning, execute manual and automated tests, and Report Defects.- Proficient in test automation tools (Selenium, Pytest), programming languages (Java, Python),version control (Git), and CI/CD basics.- Created and executed detailed test plans, identifying critical defects early in the Software Development Life Cycle (SDLC).- Contributed to Agile development cycles, working closely with product and development teams toclarify requirements and optimize QA practices.- Conducted extensive performance and scalability testing to ensure the application could handlelarge-scale inventory data and operations efficiently.

- Developed and executed automated test scripts using Selenium Web Driver with Java, ensuring 95% test case coverage for the web application.- Reduced manual testing efforts by 40% by implementing a robust automation framework, leading toquicker feedback and faster releases.- Integrated automated tests with CI/CD pipelines (Jenkins) to enable continuous testing, reducingdeployment times by 25%.- Collaborated with development teams to debug and fix issues of average defect resolution time by 20%.- Improved overall application quality and stability, contributing to a 20% increase in customersatisfaction through fewer production defects.- Used Jira to track progress on tasks, manage bugs, and collaborate with the QA team to resolve defects and ensure quality standards were met.

- Developed and implemented a B2B fintech application with AI capabilities, leveraging React JS using Java Spring Servlets and MySQL, resulting in a 50% reduction in transaction processing time, saving the team an average of 20 hours per week.- Utilized Python’s AI and ML libraries, including Pandas, to analyze financial data and optimizedecision-making processes, leading to a 15% increase in revenue generation through more accuratepredictive modelling.- Collaborated with cross-functional teams to gather requirements, prioritize tasks, and deliver features within tight deadlines, resulting in a 25% increase in project completion rate.