Data Annotation
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Object DetectionAI training and data labeling projects focused on improving machine learning and large language model (LLM) performance. Worked on large-scale annotation initiatives involving text classification, prompt evaluation, response ranking, sentiment analysis, content moderation, and data validation across structured and unstructured datasets. Supported projects involving thousands of annotation tasks and high-volume datasets in remote, fast-paced environments. I also performed tasks including data labeling, categorization, quality review, prompt-response evaluation, metadata tagging, error identification, data cleansing, and compliance validation according to project-specific annotation guidelines. Collaborated with distributed teams and project managers to ensure consistency, accuracy, and timely delivery of annotated datasets used for AI model training and optimization. In full compliance, I adhered to strict quality assurance measures, including annotation accuracy benchmarks, guideline compliance, data integrity validation, audit reviews, peer calibration exercises, and productivity KPIs. Maintained high-quality scores through attention to detail, consistency checks, and continuous feedback implementation while ensuring confidentiality and secure handling of sensitive data.