kajal chellappan

Topic: Utilizing AI methods for improved user interaction in realizing complex SIGINT system configurations. Conducted a research study on Recommender Systems using Graph Neural Networks (GNNs), improving prediction accuracy by 20-30% compared to traditional methods, using Python and machine learning libraries such as PyTorch and TensorFlow.

Developed strong proficiency in C#, mastering the Object-Oriented Programming concepts. Gained hands-on experience in creating and managing CI/CD pipelines.

Designed and implemented a scalable backend microservice architecture for Carrefour France’s financial project using Java and the Quarkus framework Designed and implemented a robust testing architecture using JUnit for unit testing and Karate and Mountebank for component testing, improving microservices reliability and functionality Facilitated knowledge transfer (KT) for new developers, which ensured a comprehensive understanding of project structures

Deputed to H&R Block as a Java Developer for the Global User Authentication (GUA) project, enhancing the authentication and authorization process using Shibboleth SSO, Conducted requirement analysis and developed authentication screens