LLM Fine-Tuning & RLHF Data Annotation for AI Assistant (NLP & Coding Tasks)
Led data annotation and evaluation efforts for fine-tuning a large language model (LLM) used in an AI assistant, focusing on improving response quality, reasoning accuracy, and user alignment. Worked extensively on RLHF (Reinforcement Learning from Human Feedback) workflows, including ranking model outputs, identifying failure patterns, and guiding model behavior toward more helpful, safe, and context-aware responses. Created high-quality prompt-response datasets for supervised fine-tuning (SFT), ensuring clarity, correctness, and diversity across domains such as general knowledge, technical topics, and programming tasks. Performed detailed evaluation and rating of model outputs based on criteria like factual accuracy, coherence, instruction adherence, and tone. Contributed to question-answering (QA) datasets by generating and validating complex queries, refining ambiguous cases, and improving dataset consistency through guideline alignment. Regularly flagged edge cases and collaborated on refining annotation standards to reduce bias and improve model generalization. Maintained high accuracy and consistency across large-scale datasets by following strict QA processes, peer review workflows, and iterative feedback loops. Comfortable working with structured annotation tools and rapidly adapting to evolving project requirements. Leveraged a strong software engineering background to understand downstream model impact, ensuring labeled data directly improved training performance, evaluation benchmarks, and real-world usability of the AI system.