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Experienced AI data specialist with a strong foundation in machine learning and natural language processing, bringing hands on understanding of what high-quality training data actually requires at the model level. Having built and fine-tuned large language models including LLaMA 2 and Mistral, I understand annotation from both sides as the engineer consuming labelled datasets and as the specialist producing them. This dual perspective enables me to deliver training data that is precise, consistent, and aligned with real model requirements, not just surface level instructions. Proficient in text classification, named entity recognition (NER), sentiment analysis, intent labelling, and bounding box annotation across NLP and computer vision tasks. Experienced working with annotation platforms and quality assurance workflows, applying clear labelling guidelines with high inter-annotator agreement. Adept at handling ambiguous edge cases, flagging schema inconsistencies, and maintaining accuracy at scale across large annotation batches making me a reliable contributor to RLHF pipelines, LLM evaluation projects, and supervised learning datasets for AI training.
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Question AnsweringAnnotated 2,000+ images for a multimodal AI training dataset, authoring precise question and answer pairs testing object identification, spatial reasoning, color recognition, and scene interpretation. Applied strict labelling guidelines maintaining 95%+ inter annotator agreement across all batches, directly supporting supervised learning pipelines for computer vision and multimodal large language model fine tuning.
