Anusha Vuppala

I’m an AI/ML Engineer with 4+ years of experience building machine learning models, scalable data pipelines, and cloud-based AI solutions. I focus on performance, scalability, and reliability, and I’ve delivered production-ready ML systems across AWS, Azure, and GCP environments.

In my current role, I designed and developed an AI-powered Incident Management System to automate incident triaging, severity assessment, and resolution recommendations. I built an end-to-end AIOps platform for automated incident detection, classification, prioritization, and troubleshooting guidance.

Previously, I worked on churn prediction and retention models, using XGBoost, LightGBM, Random Forest, and SHAP to identify key churn drivers and support retention decisions. Earlier, I developed an AI-driven smart meter health analytics platform, enabling a 23% improvement in average meter health with real-time reporting and continuous model monitoring.

My background also includes graduate-level research in AI/ML and NLP, where I built scalable preprocessing, feature engineering, and experimentation pipelines with Python and PySpark. I enjoy translating complex modeling work into practical systems—backed by strong validation, monitoring, and cross-functional collaboration.