AI Data Annotator and LLM Evaluation Specialist with experience in high-accuracy data labeling, search relevance evaluation, ad quality assessment, and AI-generated content review. Experienced in working with structured guidelines to improve the quality, reliability, and factual accuracy of machine learning training data. I have hands-on experience in evaluating AI model outputs for factual correctness, logical consistency, and usefulness, including identifying hallucinations, incorrect references, and misleading reasoning. I also create realistic domain-specific prompts that simulate real-world user behavior to support LLM training and performance improvement. My work includes scoring and ranking model responses using structured rubrics, providing detailed evidence-based justifications, and ensuring consistency in large-scale annotation tasks. I am highly comfortable working independently in high-volume environments while maintaining strong attention to detail and quality standards. Core competencies include data annotation, LLM evaluation, prompt engineering, search relevance rating, content quality assessment, and AI training data validation. I am skilled in interpreting complex English-language guidelines and translating them into consistent, accurate evaluation outputs. I am actively transitioning into roles focused on AI training data, LLM evaluation, and data quality assurance, with a strong interest in contributing to the development of reliable, safe, and high-performing AI systems.…AI Data Annotator and LLM Evaluation Specialist with experience in high-accuracy data labeling, search relevance evaluation, ad quality assessment, and AI-generated content review. Experienced in working with structured guidelines to improve the quality, reliability, and factual accuracy of machine learning training data. I have hands-on experience in evaluating AI model outputs for factual correctness, logical consistency, and usefulness, including identifying hallucinations, incorrect references, and misleading reasoning. I also create realistic domain-specific prompts that simulate real-world user behavior to support LLM training and performance improvement. My work includes scoring and ranking model responses using structured rubrics, providing detailed evidence-based justifications, and ensuring consistency in large-scale annotation tasks. I am highly comfortable working independently in high-volume environments while maintaining strong attention to detail and quality standards. Core competencies include data annotation, LLM evaluation, prompt engineering, search relevance rating, content quality assessment, and AI training data validation. I am skilled in interpreting complex English-language guidelines and translating them into consistent, accurate evaluation outputs. I am actively transitioning into roles focused on AI training data, LLM evaluation, and data quality assurance, with a strong interest in contributing to the development of reliable, safe, and high-performing AI systems.WWWW…