AI Data Annotation Specialist – NLP & Computer Vision
Contributed to a large-scale AI training project focused on improving machine learning model accuracy for natural language processing and computer vision applications. Responsibilities included labeling and annotating diverse datasets used to train supervised learning models. For NLP tasks, performed text classification, sentiment analysis, and named entity recognition (NER) on customer support conversations, social media posts, and short-form text data. Ensured consistent labeling by following detailed annotation guidelines and resolving edge cases through peer review. For computer vision tasks, annotated image datasets using bounding boxes and classification labels to identify objects, scenes, and visual attributes. Worked with datasets ranging from 10,000+ text entries and 5,000+ images, maintaining high accuracy and consistency. Adhered strictly to quality assurance standards, including multi-pass reviews, inter-annotator agreement checks, and feedback incorporation from project leads. Maintained an average annotation accuracy rate above 97% and consistently met project deadlines.