Sangam Shrestha | Portfolio

About Me

I am an experienced Software Engineer with a proven record in object-oriented programming, scalable system design, and full-stack development. Proficient in Java, Python, and C# with experience in SQL, cloud computing, and microservices. I'm passionate about renewable energy and innovation through technology.

Skills

Programming Languages: Java, C++, Python, C#, JavaScript, C, SQL
Cloud & Distributed Systems: AWS (S3, EC2, CloudWatch, RDS, IAM), SaaS, Microservices
Databases: SQL, MySQL, SQL Server, PostgreSQL
Frameworks & Tools: Spring Boot, Quarkus, .NET, Hibernate, Docker
Practices: Git, DevOps, Agile, Testing, Debugging, UI/UX Design
System Design: Data Structures, Business Process Analysis
Testing: Unit Testing, Integration Testing, Code Reviews

Experience

East Tennessee State University | Software Engineer

June 2025 - Present

Built an offline-first IEP digitization system using ASP.NET Core, Docker, PostgreSQL, and AWS.
Integrated bilingual workflows and achieved 99.9% uptime.

East Tennessee State University | Graduate Teaching Assistant

Aug 2023 - May 2025

Taught 100+ students programming and web development using C#, .NET, and JavaScript.
Boosted student engagement by 42% and mentored 3 new TAs.

BMP Infology Pvt. Ltd. | Software Engineer

Nov 2018 - Jun 2023

Developed 30+ features for ERP system using Java, Spring Boot, Angular, AWS.
Refactored 100K+ lines of code, cut defects by 30%, and improved UI usability.
Added real-time multilingual support and supported 2x traffic growth.

Education

East Tennessee State University

Master of Science, Computer Science | GPA: 3.9 | May 2025

Graduate Teaching Assistant

Projects

RMIPSS System

Developed a PWA with offline support for managing IEPs. Built REST APIs with ASP.NET Core and PostgreSQL. Integrated bilingual translation with Google Cloud API.

Online Delivery Management System

Created full-stack platform with MapBox API and Dijkstra's algorithm for optimal route finding. Integrated JWT for authentication.

Layoff Risk Prediction

Built ML model using Logistic Regression and Decision Trees to achieve 93% prediction accuracy for layoff risks.

Sentiment Analysis on Airline Reviews

Analyzed 23K+ reviews using DistillBERT with 84% sentiment classification accuracy. Identified top factors impacting user satisfaction.

Awards & Activities

Academic Excellence Award – Aug 2022
Hack By Girls 2018 – Finalist with smart parking system prototype
TechCamp Bootcamp – U.S. Embassy in Nepal, Feb 2017