LLM Code Evaluation Toolkit Developer
Developed automated pipelines to evaluate AI-generated backend code for correctness, efficiency, scalability, and clarity. Created structured rubrics and scoring metrics for reasoning quality, edge-case identification, and explanation clarity. Compared AI-generated code outputs against human-authored reference solutions and documented findings for AI model improvement. • Built evaluation frameworks using Python and Java • Focused on reasoning trace analysis for LLMs • Scored and analyzed both code and explanations • Contributed to enhancing data quality for AI model training