Data labelling specialist
Contributed to an AI training project focused on improving question-answering models. Evaluated and rated AI-generated responses for accuracy, relevance, coherence, and safety using structured guidelines and scoring rubrics. Compared multiple outputs, identified factual errors and bias, and flagged low-quality or harmful content. Processed high volumes of QA pairs while maintaining required accuracy thresholds, inter-annotator agreement standards, and strict data confidentiality.