Post LLM Training Data Annotator and Evaluator
Created and managed high-quality annotated datasets to improve large language model (LLM) performance. Performed supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) on AI models. Designed and evaluated AI agent trajectories to optimize model decision-making and task completion. • Refined prompts and established robust feedback loops for model improvement. • Built reproducible Docker-based testing and deployment environments. • Validated model behavior and integration by testing GitHub repository workflows. • Improved overall LLM response quality through systematic training data enhancement.