QA specialist
I have hands-on experience working on data labeling and AI training projects, particularly in improving AI performance for QA-related tasks. One notable project involved training Claude AI for the FLOWRMS application to support quality assurance processes on deployed tickets. My role included annotating and structuring real QA scenarios, tagging bugs based on severity and type, and refining expected vs. actual behavior to help the model better understand testing workflows. I also worked on curating datasets that reflect real-world edge cases, ensuring the AI could identify inconsistencies, reproduce issues, and suggest actionable insights. This significantly improved the model’s ability to assist with test case validation, bug triaging, and overall QA decision-making. My focus has been on aligning data annotation with practical QA use cases, ensuring that AI systems are not just accurate, but also context-aware and valuable to engineering and product teams.