About
I’m a software engineer passionate about building enterprise-grade, distributed applications that blend robust engineering with thoughtful architecture.
Currently, I’m developing distributed microservices for high-scale billing platforms at Altrium, focusing on global payment integration and system observability. Previously, I had the opportunity to build microservices in Go and React components at Circles.Life. Whatever I'm doing, I love solving hard problems with clean, maintainable code.
When I'm not at the keyboard, you'll probably find me catching up on the latest anime series, hitting the gym, or going down a rabbit hole of curious Ted-Ed videos.
Languages & Frameworks
Tools & Platforms
Experience
Jun 2024 — Present Software Engineer | Associate Software Engineer · AltriumDeveloped enterprise-grade Spring Boot microservices for global billing platforms, integrating PayPal and Stripe. Optimized batch payment APIs for high-volume transactions and implemented comprehensive observability using Prometheus, Grafana, and New Relic.
- Spring Boot
- Microservices
- RESTful APIs
- PayPal
- Stripe
- TDD
- Karate
- Docker
- Jenkins
- Kubernetes
- PostgreSQL
Jul 2021 — Jul 2022 Software Engineering Intern · Circles.LifeBuilt responsive frontend components with ReactJS and developed backend REST APIs in Go. Integrated caching and message queues for scalability, while maintaining high quality through TDD and proactive monitoring.
- ReactJS
- Material UI
- Go
- Microservices
- ELK
- New Relic
Education
2020–2024 BEng (Hons) Software Engineering — First Class
University of Westminster / Informatics Institute of Technology
2020–2024 BSc (Hons) Computer Science — Second Class (Lower)
University of Sri Jayewardenepura
Projects
CoinVision (Final Year Project)
IoT-enabled smart money-saving system using edge-based coin recognition on Raspberry Pi. Custom CNN trained on a self-curated Sri Lankan coin dataset achieving 96% accuracy. To view dataset publication click here.- Python
- TensorFlow
- OpenCV
- Linux
- Raspberry Pi
- CNN
- IoT
EngageTrack (Final Year Project)
EngageTrack is a computer vision–based student focus evaluation system for virtual classrooms that frames engagement detection as a video anomaly detection problem. It uses a spatiotemporal convolutional autoencoder combined with pose estimation to identify disengagement patterns, achieving improved performance over existing methods on the EmotiW dataset.- Python
- OpenCV
- TensorFlow
- React
- Anomaly Detection
- Auto-Encoders
- Pose Estimation
Publications
SerendibCoins: Exploring the Sri Lankan Coins Dataset
Preprint exploring a self-curated Sri Lankan coin dataset for edge-based recognition.
Articles
Installing Jupyter Notebook in Mac M1 (2023)
A comprehensive guide on setting up Jupyter Notebook on Apple Silicon.
React Form with TypeScript
Best practices and implementation details for type-safe forms in React.