QA Engineer with 4+ years in Manual Testing & Automation of IVI systems
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
QA Engineer with 4.4+ years of hands-on experience in Manual Testing and Automation support. Adept at requirement analysis, test case design, defect tracking, and reporting. Proven expertise in Software Testing Life Cycle (STLC), SDLC, Agile methodologies, and defect management tools like JIRA and VLM. Strong foundation in Core Java, SQL, and Selenium. Domain experience in In-Vehicle Infotainment (IVI) systems for automotive clients like Renault and Nissan.
Jain University
Master of Computer Applications · CS & IT
August 1, 2024 – June 30, 2024
Acharya Institute of Graduate Studies, Bangalore
Bachelor of Computer Applications
August 1, 2021 – June 30, 2021
LG Soft India Pvt. Ltd.
Test Engineer
October 1, 2022 – November 1, 2025
Bengaluru, Karnataka, India
Foster Technology.
Software Engineer
June 1, 2021 – October 1, 2022
Bengaluru, Karnataka, India
Certified in Software Testing with Core Java
Unknown
June 1, 2026 – Present
Java Automation Testing (Selenium, TestNG and Automation Frameworks)
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's experience across two companies, including a large multinational (LG Soft India), indicates adaptability to different organizational cultures. Their involvement in Agile/Scrum teams and collaboration with cross-functional teams suggests a team-oriented and iterative work approach. The breadth of skills and methodologies used aligns well with modern software development practices, indicating a good cultural fit for dynamic and collaborative environments.
Soft Skills & Operational Fit
The candidate demonstrates strong collaboration skills through participation in Agile/Scrum environments, daily stand-ups, sprint reviews, and test case review meetings. Their experience in generating daily/weekly reports and communicating progress indicates good operational fit for structured QA processes. The ability to troubleshoot and optimize execution speed suggests problem-solving and efficiency focus.