Data Labeling & AI Training Data Annotator
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SegmentationThe project involved supporting the development of machine learning and AI models by preparing high-quality training datasets through data labeling and annotation. The scope included reviewing raw data, applying accurate labels according to predefined guidelines, and ensuring consistency across large datasets. Tasks covered different data types such as text and structured data, with a focus on classification, tagging, and validation to improve model performance. In addition, the project included quality assurance activities such as reviewing annotated data for errors, correcting inconsistencies, and ensuring compliance with strict labeling standards. The work required attention to detail, adherence to instructions, and the ability to handle high-volume datasets within set deadlines. The overall goal of the project was to improve the accuracy and reliability of AI/ML models through clean and well-structured training data.