hello,
I'm a software engineer specialising in backend development for enterprise web applications. I care about building systems that are reliable, well-reasoned, and easy to maintain.
about me
I'm a software engineer based in Melbourne, focused on the backend side of web applications. Quarter-life crisis brought me to Australia, and somehow I ended up taking a Master's degree.
More than just writing code, I like understanding how things connect, why they break, and how to make them easier to build, use, and maintain over time.
Here are some technologies I've been working with:
- Java
- Spring Boot
- MongoDB / MySQL
- Javascript / Typescript
- Angular
- Kafka
- Node.js
- Prometheus / Grafana
Outside of work, my current side quests include learning how to drive manual, hiking when the weather behaves, and playing badminton with varying levels of confidence.
experience
Master of Information Technology
Deakin University
Jun 2024 – Jun 2026
- Specialising in Data Science, with coursework in machine learning, applied analytics, and data-driven problem solving.
- Exploring agentic systems, developer tooling, and practical AI applications.
- Undertaking a research and development unit building skills in independent technical investigation.
projects
aprilb.dev
This portfolio site — Next.js 16 App Router, Tailwind CSS v4 with CSS-native design tokens, FOUC-free dark mode via a pre-paint inline script, and a lazy-loaded p5.js fractal. An ongoing project and a personal playground for exploring agentic AI development workflows.
Next.js, React 19, TypeScript, Tailwind CSS v4, p5.js
Foodmate
A full-stack meal planning app. The interesting part was using Socket.io for two distinct real-time concerns — conversational AI meal suggestions via Google Gemini and collaborative grocery list editing via room-based sync — while caching Spoonacular recipe API calls in MongoDB to keep external hits low.
Node.js, Express, MongoDB, Socket.io
Automatic Categorization of Tagalog Documents
Undergraduate thesis published in the ACL Anthology (PACLIC 31, 2017). Applied SVM and NLP preprocessing to classify Filipino-language documents — a low-resource language with limited NLP coverage at the time.
Python, scikit-learn, SVM, NLP
Sudoku Solver
A Java Sudoku solver built on genetic algorithms rather than brute force. The real challenge was finding the right operator combination — tournament selection, PMX crossover, adaptive restarts — and tuning evolutionary parameters until it converged reliably across variable grid sizes.
Java, Genetic Algorithms, OOP
one more thing
let's connect.
I'm always open to a good conversation — whether it's about a role, a project, or just a chat about tech. My inbox is open.