AI Training Data Annotation & Quality Evaluation
I contributed to AI training projects focused on improving large language model (LLM) performance through high-quality data annotation and evaluation. My responsibilities included ranking multiple AI-generated responses based on relevance, accuracy, and clarity, as well as rewriting outputs to meet quality and safety standards. I consistently applied detailed evaluation rubrics to assess helpfulness, tone, and factual correctness. In addition, I performed text classification and labeling tasks, identifying harmful, misleading, or low-quality outputs while ensuring compliance with strict annotation guidelines. I also flagged edge cases and contributed to prompt testing to evaluate model limitations. This work required strong attention to detail, analytical thinking, and consistency in handling large volumes of data while maintaining high-quality standards.