AI Data Annotation & Model Evaluation
Reviewed and annotated large datasets used for training and evaluating machine learning and AI systems. Assessed model outputs for accuracy, consistency, tone, and logical alignment with detailed annotation guidelines. Identified ambiguous and edge-case samples, escalating inconsistencies in task instructions to maintain data quality. Applied structured judgment to label complex and nuanced content, consistently meeting strict quality thresholds. Delivered all work independently in an asynchronous, remote, project-based environment with minimal supervision