Yashasvi Dasupathruni
Aspiring Software Engineer | Java & Spring Boot| Exploring AI/ML
Hyderabad, India
yashasvi242005@gmail.com
I'm a Computer Science Engineering graduate at the Neil Gogte Institute of Technology, passionate about building scalable backend applications and exploring AI and machine learning. My technical experience includes Java, Spring Boot, REST APIs, and microservices, along with hands-on projects in machine learning, fraud detection, and AI-powered applications using Retrieval-Augmented Generation (RAG). During my internship as a Junior Software Engineer Intern at EPAM Systems, I contributed to backend development, RESTful API implementation, and collaborative Agile development practices. I enjoy solving real-world problems, learning emerging technologies, and turning ideas into practical solutions. I'm excited to connect with fellow developers, exchange ideas, collaborate on innovative projects, and grow through the Google Developer community.
Junior Software Engineering Intern
EPAM
- Contributed to the backend development of a travel agency platform using Java 21 and Spring Boot, applying object-oriented programming principles and design patterns.
- Developed RESTful APIs to support real-world application modules and backend functionality.
- Collaborated with a cross-functional Agile team, participating in daily stand-ups, sprint planning, and code reviews using Scrum practices.
- Applied microservices principles to build modular, independently deployable service components.
- Used JUnit and Maven to improve code quality and reliability, Git for version control and Jira for task tracking and sprint management.
Audio Forensics — Multi-Task Scene & Device Classification
Developed a multi-task Convolutional Recurrent Neural Network (CRNN) to simultaneously classify acoustic scenes and recording devices from raw audio. Achieved 97% device classification accuracy on the DCASE 2020 dataset within five training epochs and built audio preprocessing pipelines using Librosa and Pandas.
Credit Card Fraud Detection System
Built a machine learning system to identify fraudulent credit card transactions using Random Forest and XGBoost, achieving 82% recall. Applied SMOTE to address class imbalance and developed a Flask REST API with React integration for real-time fraud classification.
Medical Query Chatbot
Developed an AI-powered medical query chatbot using Retrieval-Augmented Generation (RAG) to provide context-aware responses from medical information. Integrated FAISS for semantic search and LLaMA-2 for response generation, with a Flask REST API backend to support query processing and information retrieval. Copy description
Bachelor of Engineering
Neil Gogte Institute of Technology · 9.18
NPTEL (IIT Kharagpur)
Apr 2024ServiceNow Virtual Internship Program
ServiceNow University
Nov 2025
Attendee · Oct 24, 2026