- 🎓 B.Tech in Computer Science Engineering (2025) — MAKAUT, Kolkata
- 💻 Entry-level Software Engineer with a focus on Java, Spring Boot, REST APIs & MySQL :contentReference[oaicite:1]{index=1}
- 🧠 Hands-on experience with Machine Learning, AI, and cloud-based deployments
- 🫀 Co-first author of an ML-based Cardiovascular Disease Prediction research paper published in the Brain & Heart journal (DOI:
10.36922/BH025340047) - 🔧 Comfortable working with Spring Security (OAuth2), Hibernate/JPA, JUnit, Swagger/OpenAPI, Render, Streamlit, GCP
- 🌱 I enjoy building end-to-end systems — from backend APIs to deployable, usable applications
- ⭐ Career goal: Grow into a reliable backend engineer who ships clean, maintainable, and well-tested software
- 🧩 Contributed to JabRef, a large-scale open-source reference manager built with Java/JavaFX/Gradle
- ✅ Added unit tests, updated the CHANGELOG, resolved merge conflicts, and aligned with maintainer review
- ✔️ All changes passed CI and were successfully merged into the main codebase
📌 PR is linked in my contributions on GitHub (
CodeRishiXprofile).
📅 Jan 2025 – Nov 2025 :contentReference[oaicite:4]{index=4}
🔗 Repository: Springcart
Tech: Java, Spring Boot, Spring Security, OAuth2, Hibernate/JPA, MySQL, JUnit, Swagger/OpenAPI, JS (Fetch API)
- Developed a secure RESTful backend for user registration, login, and session management using Spring Boot and Spring Security (OAuth2)
- Integrated Razorpay for payment processing within the checkout flow
- Used Hibernate/JPA for database operations and MySQL as the primary data store
- Exposed clean, well-documented APIs using Swagger
- Built a lightweight frontend with JavaScript (Fetch API) to consume REST endpoints
- Deployed on Render with uptime monitoring for smooth user browsing and secure authentication
📅 Jan 2024 – Nov 2025 🔗 Repository: https://github.com/CodeRishiX/Cardiovascularprediction
Tech: Python, Scikit-learn, SHAP, Pandas, Streamlit
- Built ML models (Random Forest, SVM, Logistic Regression) to predict cardiovascular disease risk
- Achieved 94.75% recall, 90.21% accuracy, and 91.31% F1-score, optimized for high recall to reduce missed high-risk patients
- Deployed as a cloud-based CVD risk assessment platform using Streamlit, providing an interactive interface for risk prediction
- Research outcome of this project was published in the Brain & Heart journal as a co-first author (DOI:
10.36922/BH025340047)
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🤖 AI & Machine Learning Intern – AICTE AI (Edunet Foundation)
- Worked on AI/ML concepts and built an SMS Spam Detection System using NLP
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📊 AI & Data Analytics Intern – VOIS Tech University Engagement Program
- Gained experience in conversational data analysis with Large Language Models and analytics workflows
- ☁️ Oracle – Certified Cloud Developer Professional (OCI 2025)
- 🤖 IBM SkillsBuild – Getting Started with Artificial Intelligence
- 🌩️ Google Cloud Skills Boost – Hands-on Labs & Skill Badges