Naga is a Java Engineer with experience in providing detailed technical designs to develop complex information systems - defining system scope and using emerging technology solutions on projects. He works on new programs - analyzing, developing, designing, and maintaining products. He enforces coding standards and best practices - reviewing application code to ensure consistent quality.
Worked on a Swing-based application that takes stream input from multiple FX Trade repositories and calculates fluctuations in the net position of the G10 currencies in real-time - applying aggregations at trade, currency, legal entity, etc. It uses the JFreeChart framework in delivering interactive charts that help traders make informed decisions for analysis. Designed and implemented the entire backend module on the app and ensured performance testing of solutions. Technologies: Java, Oracle 10G; Frameworks: Esper Framework.
Jive is a commercial Java EE-based Enterprise 2.0 collaboration and knowledge management tool produced by Aurea Software. It integrates the functionality of online communities, microblogging, social networking, discussion forums, blogs, wikis, and IM under one unified user interface. Worked on the Professional Services Team that deals with customer issues and requests that fall outside the scope of Central Support. The team handled customizations, services (tuning, upgrades, installations, configurations, integrations, and authentication), and customer-requested extensions. Technologies: Java, Kubernetes, Apache, Tomcat; Frameworks: Dropwizard Framework, Spring IoC, Struts 2.3.
Designed and implemented solutions on a recommendation system that offers users of American Express credit cards personalized rewards based on different characteristics. Participated in streamlining the development and deployment processes on the project through CI/CD - implementing data sharding and fault tolerance into the application. Worked on a module using Stochastic Gradient Descent over a multi-layered Artificial Neural Network to optimize the weights applied to the features by various recommendation systems within American Express. Technologies: Hadoop, HBase, Hive, Python
Machine Learning Techniques: XGBoost, SGD, ANN.