Rahil Shah

Increased automated test coverage by 70% by building end-to-end QA automation pipelines that streamlined the testing process across multiple platforms and applications.

Reduced manual testing effort by 60% and cut defect leakage by 35% by designing a multi-agent workflow system where separate agents planned, executed, and verified test cases automatically.

Improved test-case accuracy by 45% by integrating AI-powered visual understanding that enabled the system to analyze and interpret images, diagrams, and interface screenshots during testing.

Achieved 89% prediction accuracy (ROC-AUC: 0.89) and 87% F1-score by developing and deploying machine learning models to detect and flag system performance issues proactively.

Improved model accuracy by 30–35% by building data preprocessing and feature engineering pipelines that cleaned, structured, and optimized raw datasets for downstream analysis.

Increased anomaly detection precision by 42% and reduced debugging time by 50% by implementing an intelligent log-clustering system that automatically grouped similar error patterns for faster resolution.