AI Data Annotation Specialist
Evaluated multi-language code samples for AI model training, ensuring accuracy and logical correctness across Python, JavaScript, Java, and C++. Conducted detailed debugging and analysis of model outputs, providing actionable feedback to improve dataset quality. Worked collaboratively with distributed machine learning teams to enhance training data and prompt engineering. • Verified and rated code according to best practices and reproducible results • Documented technical reasoning and communicated findings with metacognitive precision • Used Labelbox and OpenTrain AI platforms for code annotation and review • Contributed to the continuous refinement of AI training datasets by identifying architectural flaws and dataset gaps.