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QA Automation Engineer with 7+ years in AI-driven & enterprise applications
Quality Assurance (QA) Engineer with over 6 years of experience in manual and automation testing across web, enterprise, and AI-driven applications. Proficient in designing robust automation test suites using Selenium and API testing frameworks, with strong expertise in regression, integration, performance, and end-to-end testing within Agile environments. Certified ISTQB Foundation Level Tester with hands-on experience in validating AI-powered systems, including LLM-based applications and Retrieval-Augmented Generation (RAG) pipelines. Skilled in evaluating response accuracy, contextual grounding, tool workflows, and performance metrics to ensure reliable, scalable, and production-ready software solutions. Passionate about strengthening quality engineering practices across both traditional and emerging AI systems.
Institute of Management and Technology
Master of Computer Applications (MCA)
August 1, 2014 – August 1, 2016
DAV Centenary College
Bachelor of Computer Applications (BCA)
July 1, 2011 – July 1, 2014
Gigaforce
QA Engineer
May 1, 2024 – Present
Noida, Uttar Pradesh, India
Kent RO Systems
Associate (QA)
June 1, 2022 – July 1, 2023
Noida, Uttar Pradesh, India
Gravity Automation
Software Tester
March 1, 2019 – March 1, 2022
Noida, Uttar Pradesh, India
Competent Software Pvt. Ltd.
Associate (QA)
June 1, 2017 – March 1, 2018
Faridabad, Haryana, India
Automation Testing Using Selenium
Internshala
October 1, 2023 – Present
ISTQB Foundation Level
ISTQB
September 1, 2023 – September 1, 2025
Cultural Fit Analysis
The candidate's experience across various companies, including a startup (Gigaforce) where they were the first technical hire, indicates adaptability and a proactive mindset. Their involvement in diverse projects, from insurance enterprise software to AI-powered chatbots, shows a broad interest in different domains. The certifications in ISTQB and Selenium further highlight a commitment to professional development and best practices, aligning well with a culture that values continuous learning and quality.
Soft Skills & Operational Fit
The candidate demonstrates strong operational fit through their experience in Agile environments, cross-functional collaboration, and leadership in implementing end-to-end testing processes. Their ability to manage weekly releases and prioritize defects indicates good organizational and problem-solving skills. The detailed descriptions of AI/LLM testing methodologies suggest a methodical and analytical approach to complex problems.