Sujan Acharya

Java Backend Developer

I am a Java backend developer focused on building real-time projects using Spring Boot, microservices, and database integration with MySQL and PostgreSQL.

I enjoy working on scalable systems, API development, and exploring cloud platforms such as AWS and Azure. I am also interested in deep learning and neural networks. Check out my work on

Skills

java logo Java
spring-boot Spring Boot
git logo Git
bitbucket logo Bitbucket
html logo HTML
Css logo CSS
javascript logo JavaScript
python logo Python
mysql logo Mysql
postgresql logo PostgreSQL
mongodb logo MongoDB

Projects

Stripe Payment Integration System

Technologies: Spring Boot, Stripe API, MySQL, Jira (Scrum), Bitbucket, Git

Currently developing a microservices-based payment integration system using Spring Boot and Stripe API. The application securely exposes payment-related REST endpoints and manages transactions with MySQL as the primary database. The architecture leverages Spring Cloud Config Server for centralized configuration, an API Gateway for secure routing, Circuit Breakers for fault tolerance, and distributed logging/monitoring for observability across services. Following Agile methodology with Jira for sprint planning and version control with Git.

Bus Booking Management System

Technologies: Spring Boot, PostgreSQL, JWT Authentication, HTML, CSS, JavaScript

Currently developing a full-stack bus booking management system with role-based access. The platform supports three roles: Admin (manages agents and routes), Agents (manage buses and schedules), and Users (search and book tickets). Implemented authentication and authorization with JWT to secure the application. The backend is powered by Spring Boot and PostgreSQL, while the frontend is plain HTML, CSS, and JavaScript.

View in Github

Past Projects

Crop Price Prediction using LSTM (Long Short Term Model)

crop-price-prediction-using-lstm-model

A time series prediction project that forecasts crop prices for Arecanut (Coca) and Coconut (Grade-I) in the Mangaluru market. The system uses historical government data (2015–2022) and applies an LSTM deep learning model to predict future modal prices. Deployed on Streamlit for interactive use.

  • Used historical market data from government sources.
  • Preprocessed data, handled missing values, and extracted features.
  • Built and trained an LSTM neural network for time series forecasting.
  • Evaluated model performance and deployed with Streamlit for real-time predictions.
View project

Form Filling Automation (OCR + NER)

entity extraction using ocr and ner

An Android application that automates form filling by extracting relevant information from uploaded documents. The app combines Optical Character Recognition (OCR) and Named Entity Recognition (NER) techniques to identify important fields such as names, dates, emails, and more.

  • Scans documents using OCR to extract raw text.
  • Applies NLP-based NER to identify entities like names, addresses, dates.
  • Regex rules enhance detection for patterns such as emails and phone numbers.
  • Automatically fills structured forms with extracted data, reducing manual effort.
View Project

Work Experience

Java Developer Trainee

Ongoing

August 2024 – Present

Contributing to the development of a microservices-based payment integration system using Spring Boot and the Stripe API. Responsible for backend development, database integration, and API design to ensure secure and reliable payment workflows.

  • Designed and implemented REST APIs for handling payment transactions.
  • Integrated MySQL as the primary database with Spring Data JPA.
  • Worked with Spring Cloud Config Server, API Gateway, and Circuit Breakers for scalability and fault tolerance.
  • Followed Agile methodology with Jira for sprint planning and Git for version control.

AI/ML Intern

1-Month Internship

Gained hands-on experience with the fundamentals of machine learning and deep learning, including supervised and unsupervised learning techniques. Built and deployed an end-to-end project leveraging neural networks for predictive analysis.

  • Explored data preprocessing, feature engineering, and model evaluation techniques.
  • Developed and trained deep learning models using Python libraries such as TensorFlow and Keras.
  • Deployed a complete ML project for real-world usage and presented outcomes effectively.