Worked on an RLHF (Reinforcement Learning from Human Feedback) pipeline focused on dataset creation, data annotation, and model evaluation. My role involved designing and curating high-quality prompt datasets, reviewing AI-generated responses, and providing structured feedback based on accuracy, relevance, safety, and helpfulness. Contributed to improving model performance by ensuring consistent evaluation standards and high-quality human feedback for training alignment and refinement.
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I provide high-quality software maintenance and optimization services, focusing on identifying and resolving complex technical issues. My work ensures that applications remain fast, reliable, and scalable. By applying clean code principles and rigorous debugging processes across Python, C++, and React environments, I help clients improve their software's performance and user experience.