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I specialize in AI data annotation, LLM evaluation, and guideline‑based scoring across multiple platforms. My work focuses on accuracy, consistency, and clear reasoning, including safety review, bias detection, multi‑step reasoning checks, and content labeling. I follow instructions exactly as written and maintain high quality across repetitive or detail‑heavy tasks. I have hands‑on experience with annotation tools, evaluation rubrics, and structured workflows used in AI training projects. I’m reliable, fast‑learning, and able to work independently without supervision. My strength is producing clean, high‑quality judgments that help improve model performance and alignment.
Video
Evaluation/RatingI evaluate AI‑generated or AI‑related video content by reviewing accuracy, visual clarity, safety compliance, and alignment with task instructions. This includes identifying issues, rating quality, and applying structured guidelines to ensure consistent evaluation.
AudioEvaluation/RatingI evaluate AI‑generated audio responses by checking clarity, accuracy, tone, and compliance with instructions. Tasks include identifying errors, rating quality, and applying detailed guidelines to ensure the audio output meets required standards.
TextEvaluation/RatingI evaluated AI model responses by comparing multiple outputs and selecting the most accurate, safe, and guideline‑compliant answer. Tasks included checking reasoning quality, identifying safety issues, scoring responses based on written rubrics, and ensuring outputs followed instructions. This work required attention to detail, consistency, and the ability to apply complex rules across repetitive tasks.
