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Data CollectionAI Data Labeler (Fresher) — Image Labeling. Core strengths include N and A. Education includes Bachelor of Technology, Godavari Institute of Engineering and Technology (2023). AI-training focus includes data types such as Image, Text, and Audio and labeling workflows including Classification, Evaluation, and Rating.
DocumentPerformed quality control and AI response evaluation by reviewing data for accuracy and consistency and rating chatbot outputs. Focused on detecting labeling issues early and ensuring annotated or evaluated items met expected standards. • Quality control review • Data accuracy and consistency checks • Chatbot response evaluation and rating • Error identification for rework
AudioClassificationCarried out audio labeling by identifying sounds and categorizing audio clips for supervised learning datasets. Reviewed labeled audio data to reduce errors and improve dataset reliability. • Sound identification • Audio clip categorization • Quality review of annotations • Dataset readiness support
TextClassificationCompleted text annotation tasks including classifying text and sentiment into positive, negative, or neutral categories. Applied careful review practices to maintain label accuracy and uniformity across examples. • Text classification • Sentiment labeling (positive/negative/neutral) • Attention to annotation quality • Consistency checking
ImageClassificationPerformed image labeling by identifying and tagging objects such as cars, animals, and people for dataset annotation and model training readiness. Ensured each labeled item met the required annotation guidelines and contributed to consistent training data. • Object identification and tagging • Dataset annotation support • Consistency with labeling guidelines • Preparation for downstream model training
Bachelor of Technology, Engineering
Aspiring Data labeler
