Rajesh Chodavarapu
About Rajesh Chodavarapu
Rajesh Chodavarapu is a Data & Machine Learning Engineer at HTC Global Services, with extensive experience in various roles at Tata Consultancy Services and a strong educational background in Data Analytics and Computer Applications.
Current Position at HTC Global Services
Rajesh Chodavarapu currently works at HTC Global Services as a Data & Machine Learning Engineer. He has been with the company since 2019 and is based in the Bloomington/Normal, Illinois area. His role involves working with data and developing machine learning models.
Experience at Tata Consultancy Services
Rajesh Chodavarapu has had an extensive career at Tata Consultancy Services (TCS) across various roles and locations. He began as an Assistant System Engineer in Chennai, Tamil Nadu, India, from 2010 to 2012. He then served as a System Engineer in Hyderabad Area, India from 2012 to 2015. Moving to San Antonio, Texas, Rajesh worked as ITA from 2015 to 2018 and later as an Assistant Consultant/Digital Marketing Analyst from 2018 to 2019.
Educational Background
Rajesh Chodavarapu holds a Master's degree in Data Analytics from The University of Texas at San Antonio, completed between 2017 and 2019. He also obtained a Master of Computer Applications (MCA) from Gokaraju Rangaraju Institute of Engineering and Technology from 2007 to 2010. Earlier, he earned a Bachelor of Science in Chemistry from New Scholars Degree College, where he studied from 2004 to 2007.
Previous Roles and Research Experience
Before his current tenure at HTC Global Services, Rajesh Chodavarapu worked briefly as a Junior Software Developer at Infinity Software Solutions in Hyderabad, Telangana, India, in 2010. Additionally, he served as a Research Associate at Gokaraju Rangaraju Institute of Engineering and Technology from 2009 to 2010. He also interned at Tata Consultancy Services in Hyderabad Area, India, for four months in 2010.
Key Projects and Contributions
During his career, Rajesh Chodavarapu has developed a reusable framework for Machine Learning model deployment and monitoring using Flask and GitLab pipelines. He has also created a proof of concept to detect anomalies in application usage and automated test data creation by developing a Machine Learning model using Python and Scikit-learn. These initiatives have led to significant savings in person hours and money.