iOS Developer · Moving towards full-stack development, Naples, Italy
I build native iOS apps using Swift and SwiftUI and publish them on the App Store. I’m studying automation engineering and expanding my skills in web and backend development using React, TypeScript, Python, and PostgreSQL. I enjoy turning real-world problems into simple, useful products.
- Apple Developer Academy @ UniNa: preparatory year, followed by the Pier Program (Advanced Developer)
- I released CowPow! Radio Stories worldwide on the App Store, created for CHOC (Childhood Cancer Foundation, South Africa)
- I’m currently learning: Node.js and Express, to round out the backend side of full-stack development
- I’m looking for: roles as an iOS or full-stack developer, in Italy or remotely
iOS
Web & backend
Automation & tools
| Project | Description | Stack |
|---|---|---|
| CowPow! Radio Stories | A medical simulation game that helps children prepare for radiation therapy, developed for CHOC (South Africa) and distributed worldwide | Unity, C#, SwiftUI |
| Fixo | Mentoring project: As team lead, I guided a group of Foundation Year students at the Apple Developer Academy from the initial idea to the creation of a working iOS app | SwiftUI, Supabase |
| MyAcademy | Gamified app for weekly challenges and rewards in an educational setting, with mission completion via photos and full VoiceOver support | SwiftUI, Accessibility |
| Porting from iOS to Android | Technical assessment: a SwiftUI app rewritten natively for Android, mapping each view and ViewModel to its counterpart in Compose | Kotlin, Jetpack Compose |
| AI-powered email agent | Categorizes incoming emails using Claude AI and automatically forwards them, with human approval on Discord before sending urgent replies | n8n, Claude, Notion |
| ETL Pipeline | A scheduled pipeline that retrieves cryptocurrency prices from a public API, transforms them, and loads them into PostgreSQL via REST | n8n, PostgreSQL, Supabase |
| Alzheimer’s Classification | Compares 9 machine learning-based classifiers to predict dementia status using longitudinal clinical data | Python, scikit-learn |
Italian (native) · English (fluent)