I engineered multilingual QA governance and RAG evaluation systems for frontier models at Turing, optimizing personalization across 5,000+ data samples. I built a scalable training system by designing structured error taxonomies and automated debug metadata workflows. Key Architectural Results: • Model Optimization: Cut model output inconsistencies by 18% through multi-turn SxS evaluation. • Sprint Alignment: Synchronized cross-functional teams with rapid Agile RLHF deployment cycles. • Risk Management: Mitigated hallucinations by tracking semantic drift and grounding metrics.