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Object DetectionI have extensive experience across the entire AI training and data labeling lifecycle. My background includes high-quality data annotation, RLHF (Reinforcement Learning from Human Feedback), and dataset curation. I specialize in evaluating model outputs, drafting clear task guidelines, and refining complex datasets to improve AI accuracy, reasoning, and conversational tone. Additionally, I excel in domain-specific tuning, adversarial testing (red-teaming) to identify vulnerabilities, and aligning system behaviors with strict safety and quality standards. This hands-on experience allows me to quickly deconstruct user instructions, adapt to specific formatting constraints, and deliver precise, actionable information.
One of the most foundational AI training and data labeling experiences is working on Natural Language Processing (NLP) evaluation. This process, often referred to as Supervised Fine-Tuning (SFT) or Reinforcement Learning from Human Feedback (RLHF), requires human annotators to review, rate, and refine responses generated by Large Language Models (LLMs).
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data annotation
