I specialize in Reinforcement Learning from Human Feedback (RLHF), optimizing AI models through high-quality data curation, prompt engineering, and systematic evaluations. My work involves analyzing complex coding and STEM concepts, refining model-generated outputs, and ensuring technical accuracy while adapting to evolving project requirements.
- Developed and optimized training datasets for large language models, focusing on code generation, analysis, and validation.
- Designed and implemented advanced prompting strategies to enhance model reasoning and contextual accuracy across Python and JavaScript.
- Utilized machine learning libraries and frameworks to preprocess, validate, and optimize AI-generated outputs for improved performance.
- Conducted extensive code reviews, debugging, and error analysis to refine AI-generated solutions and ensure adherence to best coding practices.
- Engineered robust data pipelines and automated workflows for processing and maintaining high-quality training data.