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QA Engineer with 5+ years in IoT, AI & Mobile Testing
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
5+ years of experience as a QA Engineer in IoT domain, AI, LLM, TTS, STT domain. Excellent experience in Manual, Firmware testing, UAT, Frontend automation, API & Mobile Application testing. QA Engineer specializing in AI-driven Voice Assistants and Conversational Chatbots. Experienced in developing automation test scripts with help of Katalon Studio, Page Object Model, Page Factory, Maven & TestNG Framework on Eclipse IDE. Experience in Bug Reporting, Bug Verifying & Bug Tracking on JIRA & TestRail tools. Excellent experience in the Quality Assurance and Quality Control life cycle. Experienced in validating NLU, intent recognition, conversation flows, and speech-to-text accuracy. Competence Waterfall and Agile (Scrum) methodologies.
GGSIPU
B.Tech · Information Technology
N/A – June 30, 2022
Adarsh Public School
INTERMEDIATE
N/A – May 31, 2017
Adarsh Public School
10TH STANDARD
N/A – May 31, 2015
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Cultural Fit Analysis
The candidate's experience spans different company sizes and industries (AI, IoT, real estate), indicating adaptability. Their involvement in various testing types (manual, automated, firmware, API, mobile, web) and platforms (Android, iOS, Windows) shows a broad skill set and willingness to tackle diverse challenges. The progression from QA Engineer to QA Lead suggests leadership potential and a growth mindset. However, the resume does not provide explicit details on cultural values or team interaction styles beyond participation in meetings.
Soft Skills & Operational Fit
The candidate demonstrates strong operational fit through their experience in Agile/Scrum environments and collaboration with project managers and hardware engineers. Their ability to assist in resource estimation and provide insights in meetings indicates good team collaboration and proactive engagement. The experience in validating complex AI systems suggests a detail-oriented and analytical approach.