Md. Rifat Ahasan Pulock
Backend Engineer · CSE undergraduate · Dhaka, Bangladesh
Computer Science and Engineering undergraduate focused on Java/Spring Boot backend development, real-time systems, and full-stack software engineering. My work spans production-minded APIs, PostgreSQL-backed applications, WebSocket systems, AI-enabled e-commerce, and interactive ML applications.
Education
United International University
BSc in Computer Science and Engineering
Jan 2023 — Feb 2027 (expected) · CGPA 3.06 / 4.00
Relevant coursework: Data Structures & Algorithms, Object-Oriented Programming, Software Engineering, Operating Systems, Artificial Intelligence, Machine Learning, Data Mining, Digital Image Processing, Linear Algebra
Selected engineering projects
Eid Cricket Fest
A production-minded cricket tournament platform supporting tournament management and live scoring through a Java/Spring Boot backend and Next.js frontend.
- Integration tests start PostgreSQL automatically through Testcontainers.
- Live match updates are published on /topic/matches/{matchId}.
- Live payloads carry inningsId and scoreRevision so clients can ignore stale messages.
ShopyOnline
An AI-powered PERN e-commerce application using Google Gemini for natural-language product search, PostgreSQL for relational data, Stripe for payments, and React for the customer and admin interfaces.
- Backend builds parameterized SQL WHERE clauses dynamically from active price, category, availability, rating, and text filters.
- Filtering performs a COUNT query for pagination followed by a parameterized SELECT with LIMIT/OFFSET.
- Stripe PaymentIntent handling reuses valid intents instead of blindly creating new ones.
Heart Disease Prediction & Analysis
A Streamlit-based machine-learning application that loads a trained heart-disease model, accepts patient features, predicts class and probability, and presents risk information through an interactive interface.
- Repository includes a serialized optimized model, feature metadata, and the interactive Streamlit application.
- Application uses model.predict and model.predict_proba to generate both a class prediction and percentage risk probability.
- UI exposes model metadata including model type, accuracy, and feature count when loaded.
Technical skills
Languages: Java, Python, JavaScript, SQL, C, C++
Backend systems: Spring Boot, Spring Data JPA, Hibernate, REST APIs, Spring Security, JWT, WebSocket, STOMP, Node.js, Express
Interfaces: React, Next.js, Redux Toolkit, Tailwind CSS, Vite
Databases: PostgreSQL, MySQL
Machine learning: PyTorch, scikit-learn, NumPy, pandas, PCA
Engineering & delivery: Docker, Git, GitHub, GitHub Actions, Flyway, Testcontainers, Railway, Vercel, Jupyter
Certifications
Neural Networks and Deep Learning
DeepLearning.AI · May 2026
Credential: JDSMSGZE6050
Supervised Machine Learning: Regression and Classification
DeepLearning.AI · Mar 2026
Credential: HYA64D9EOONE
NodeJS Masterclass (Express, MongoDB, OpenAI)
Udemy · Nov 2024
Credential: UC-7354ce18-fec3-4eee-9184-fd8ada16e570
Master Git and Github - Beginner to Expert
Udemy · Jul 2024
Credential: UC-16db975c-e53a-49bf-b954-f8755bd5a3a6