AI Language Evaluation & Data Annotation Specialist
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Evaluation/RatingContributed to an AI training and data annotation project focused on improving the performance of large language models (LLMs). The project involved reviewing, labeling, and evaluating thousands of AI-generated text responses across multiple domains. Key tasks included text classification, content evaluation, intent recognition, relevance scoring, error identification, fact-checking, and quality assessment based on detailed annotation guidelines. Worked with large-scale datasets consisting of conversational, informational, and instruction-based content. Maintained high accuracy and consistency through regular quality assurance reviews, adherence to annotation standards, and self-auditing procedures. Achieved project objectives by delivering precise annotations, meeting productivity targets, and ensuring data integrity for machine learning model training and evaluation.