Defined QA standards and code review rubrics for AI automation systems. Led ML quality audits, designed gold-standard ML scripts using Hugging Face and OpenCV, reviewed codebases, and delivered AI governance dashboards with SHAP-based interpretability.

Designed ML QA framework adopted across 5 AI annotation projects (NLP, CV, Recommenders). Built explainability layer for anomaly detection models, improving bias transparency by 30%.

Oversaw ML code reviews, created frameworks for data cleaning and feature engineering, built financial forecasting and anomaly detection models, and authored ML evaluation documentation.

Introduced structured review process for AI model deliverables, cutting review time by 40%. Delivered ML documentation and calibration rubrics for 4 major AI clients.

Developed predictive maintenance ML systems, designed ETL data pipelines, conducted model validation, and produced training documentation on model explainability and XAI tools.

Authored first internal ML evaluation checklist adopted as company-wide quality standard. Implemented QA monitoring workflows improving ML pipeline accuracy by 15%.