AI Model Evaluation & Data Labeling
Worked on AI training and evaluation tasks focused on code understanding, data science problem design, and model output validation. Designed and annotated datasets involving Python-based data science workflows, including data processing, statistical analysis, and machine learning tasks using libraries such as Pandas, NumPy, and Scikit-learn. Performed evaluation and labeling of AI-generated outputs (LLMs) for correctness, efficiency, and reasoning quality across multiple domains, including backend systems and analytics pipelines. Created deterministic problem sets with reproducible solutions, ensuring consistency by controlling randomness and validating outputs using Python scripts. Contributed to improving model performance by identifying edge cases, refining prompts, and providing structured feedback on model behavior. Collaborated in iterative development cycles using AI tools (Claude, Cursor) to accelerate annotation, validation, and dataset generation processes.