AI Data Annotation & Model Training Support Project
Worked on multiple AI data labeling and annotation projects supporting machine learning and large language model development. Responsibilities included annotating and reviewing text, image, and audio datasets to improve model accuracy and performance. Performed tasks such as text classification, named entity recognition (NER), sentiment and emotion labeling, response quality evaluation, and prompt–response generation for supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF). Additionally contributed to coding-related data annotation by evaluating programming responses, identifying logical errors, and rating generated outputs. Handled datasets ranging from thousands to large-scale multi-domain samples while maintaining strict annotation guidelines and consistency standards. Followed quality assurance processes including double-review validation, guideline adherence checks, and inter-annotator agreement practices to ensure high-quality labeled data.