Shiv C.

I have extensive experience in AI training data, data labeling, and reinforcement learning from human feedback (RLHF), specializing in refining and evaluating machine learning models, particularly large language models (LLMs). My work has involved annotating and curating high-quality datasets, ranking AI-generated responses based on relevance and quality, and identifying biases or inconsistencies to improve model alignment. Through hands-on experience with annotation platforms and structured evaluation frameworks, I have developed a strong ability to assess AI outputs for accuracy, coherence, and fairness.In addition to data labeling, I have worked on RLHF-driven model fine-tuning, helping AI systems learn from human preferences to produce more contextually appropriate and user-aligned responses. My expertise includes prompt engineering, response ranking, bias detection, and ethical AI considerations, ensuring AI-generated content meets high standards of accuracy and inclusivity. With a detail-oriented approach and a deep understanding of AI behavior, I am passionate about enhancing AI reliability, bridging the gap between human expertise and machine learning advancements.