Expert Data Annotator & AI Model Evaluator (Independent Contractor — Multiple AI Platforms)
TextServed as an Expert Data Annotator and AI Model Evaluator performing structured labeling and AI output quality assessments for text-based inputs. Used rubric-based scoring to evaluate factual accuracy, logical consistency, and language quality while applying domain expertise to determine label appropriateness. Documented citation-supported decisions, flagged ambiguous or out-of-scope samples, and escalated issues with clear written justifications to reduce downstream training errors. • Annotated text/documents and model-generated outputs using structured labeling frameworks and project guidelines • Provided citation-supported reasoning and source justifications for each label/evaluation choice • Applied ambiguity flagging and escalation with written explanations for inconsistent or unclear samples • Produced actionable feedback that informed model improvement based on rubric-based scoring