Full-Stack Developer & Software Engineer
Experienced Full-Stack developer with 6 years of experience in developing applications using .NET Core, Angular, SQL, and PostgreSQL. Specialized in building predictive models in Python and deploying containerized applications to Kubernetes in Cloud using CI/CD.
Building reliable systems, one layer at a time.
I'm a Full-Stack Developer with 6 years of experience at Gadgeon Systems Inc., specializing in healthcare systems, fleet management, and shipping solutions. I have expertise in modern web technologies and cloud deployment, with a strong focus on performance optimization and system architecture.
My experience includes working with microservice architecture, implementing CI/CD pipelines, and integrating various third-party APIs. I'm passionate about learning new technologies and solving complex technical challenges.
Years Experience
Projects Completed
Technologies Mastered
The tools and platforms I work with day to day.
A selection of professional, personal, and research projects across web, ML, and tooling.
A multi-module web application built on Angular and .NET Core, backed by PostgreSQL. Handles real-time data synchronisation across distributed services, with third-party API integrations for logistics providers via RESTful endpoints.
Extended a microservices-based platform to support new IoT device protocols and GPS telemetry ingestion. Built .NET Core APIs deployed on Kubernetes with Helm, optimised via CI/CD pipelines for zero-downtime rollouts.
A full-stack workflow orchestration platform built with React and Java Spring Boot. Uses Flowable as the BPMN process engine, PostgreSQL for persistence, and Keycloak for OAuth2/OIDC identity and access management.
End-to-end ML pipeline built on AWS SageMaker for real-time anomaly scoring. Orchestrated inference via AWS Lambda and API Gateway; trained models on structured tabular data using scikit-learn and XGBoost.
A convolutional neural network trained to classify images into character identities. Built with Python and Keras using transfer learning on a custom-labelled dataset, with image augmentation applied for generalisation.
A binary classification pipeline using scikit-learn to detect anomalous transactions in highly imbalanced tabular datasets. Applied SMOTE for class balancing and evaluated models using precision-recall AUC.
A ground-up implementation of linear regression using NumPy — gradient descent, cost function minimisation, and model evaluation — without any ML framework abstractions.
Implementations of major unsupervised clustering algorithms — K-Means, DBSCAN, and Agglomerative — benchmarked across synthetic and real datasets with visualised cluster boundaries.
A reference collection of classic computer science algorithms implemented in Python — sorting, graph traversal, dynamic programming, and search — each with time and space complexity annotations.
A native macOS menu bar application built in Swift that polls a local network device's status API, surfacing battery and signal metrics directly in the system tray without opening a browser.
A Python CLI tool that authenticates with the Reddit OAuth2 API via PRAW to fetch, filter, and export a user's saved posts and comments into structured formats for offline archiving.
I'm always interested in new opportunities and exciting projects. Feel free to reach out if you'd like to collaborate or just want to say hello.