Pradyumna Gadepally

Designed and implemented efficient data preprocessing functions to optimize the machine learning pipeline performance, including feature engineering steps and data validation. Developed one-hot encoding and categorical clipping to improve model accuracy and inference efficiency. Identified and remediated security vulnerabilities in the codebase to strengthen system integrity and data protection. Led collaboration with platform teams to migrate infrastructure from on-prem to cloud (Azure), ensuring seamless transition and minimal downtime during the transition. Refactored data pipelines by integrating MongoDB and replacing ElasticSearch to enhance scalability and query performance. Improved code quality by adding comprehensive documentation, robust error handling, and structured logging. Managed debugging and testing processes, and successfully deployed models and workflows as Azure Databricks jobs and pipelines. Gained competence with GitHub CoPilot and related tooling to accelerate co

Assisted in delivering the Foundations of Machine Learning course, including leading office hours, grading assignments, and supporting course administration. Contributed to student success by clarifying concepts, providing feedback, and coordinating with course staff to improve learning outcomes.

Designed and developed both front-end and back-end of a web platform using Django, JavaScript, MongoDB, and Vue.js. Conducted system testing and optimization to ensure robust performance and a seamless user experience.