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QA Engineer with 6+ years in Automation & AI/ML Validation
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QA Engineer with 5+ years of experience specializing in end-to-end quality assurance across E-commerce, Logistics, Finance, and Warehouse Management Systems (WMS). Strong expertise in both Manual and Automation Testing, including Functional, Regression, UI, API, and End-to-End testing. Proficient in Selenium-based automation, Postman API validation, and SQL-based backend testing. Hands-on experience in AI/ML and LLM validation, including prompt engineering, hallucination detection, and output evaluation. Experienced in Agile methodologies, CI/CD environments, and test lifecycle management using JIRA and Azure DevOps. Focused on delivering high-quality releases through early defect detection, automation optimization, and data-driven QA practices.
Misrimal Navajee Munoth Jain Engineering College, Anna University
B.E. · Electronics and Communication Engineering
August 1, 2015 – June 30, 2019
Cognizant
Associate (QA Engineer Role)
June 1, 2020 – Present
India
Cultural Fit Analysis
The candidate's experience across diverse domains (E-commerce, Logistics, Finance, WMS) and exposure to AI/ML/LLM validation suggests adaptability and a broad technical interest, which can contribute positively to a dynamic team environment. Their role preference aligns well with the target QA Engineer role, encompassing manual, automation (Playwright/Python/Selenium), API, and hybrid QA roles. The focus on optimizing test coverage and improving reliability of AI-driven features indicates a proactive and quality-driven mindset, fitting well into a culture that values innovation and continuous improvement.
Soft Skills & Operational Fit
The candidate's resume highlights active participation in Agile ceremonies (Sprint Planning, Grooming, Daily Stand-Ups, Retrospectives), indicating good team collaboration and communication skills within an Agile framework. The ability to deliver dashboards and reports for stakeholders suggests good communication and reporting skills. The achievement of reducing manual testing effort by 60% and identifying critical defects points to a proactive and results-oriented work attitude. The experience in validating AI/ML and LLM workflows demonstrates adaptability and a willingness to work with emerging technologies.