I'm a Software & Solutions Engineer at QVCCS, working across backend services, full-stack applications, and system integrations. I hold an MSc in Advanced Computer Science (Distinction) and a BSc (Hons) in Computer Science (2:1) from the University of Kent — my MSc dissertation shipped Snackless, a Flutter behavioural-programme app used by real participants to complete a 30-day intervention.

My work spans cloud platforms, mobile apps, IoT firmware, and increasingly on-device AI tooling. I care about clean architecture, sensible abstractions, comprehensive testing, and shipping software people actually use. Outside delivery work I sharpen technique through deliberate side projects — usually in unfamiliar stacks — and treat each one as a chance to raise the bar on quality and maintainability.

Open to conversations about Graduate / Junior Software Engineer roles where I can apply this breadth, work with thoughtful teams, and keep growing.

Languages

Frameworks & runtimes

Data & infrastructure

Tools & platforms

Privacy-first Teams transcription & AI summaries on macOS.

A lightweight macOS daemon that records Microsoft Teams meetings, transcribes them locally with Whisper, and produces structured summaries via Claude or Ollama — fully offline-capable. Includes speaker diarisation, Notion / Obsidian export, and a clean YAML schema for downstream tooling.

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Adherence analytics for daily dosing.

A modern SvelteKit 5 full-stack app for logging doses and tracking adherence over time. PostgreSQL on Neon with Drizzle ORM and Lucia auth; an analytics dashboard surfaces 90-day heatmaps and hourly distribution patterns. End-to-end tested with Vitest and Playwright, deployed on Vercel.

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Personal site, brutalist design, long-form case studies.

Next.js 16 portfolio with a distinctive brutalist visual identity, Framer Motion micro-interactions, Server Actions for the contact form (via Resend), generated Open Graph images, and accessibility-first semantics. Hosts case studies that walk through the technical decisions behind each project.

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Embedded ML and PID control for autonomous plant care.

An Arduino / ESP32 system using real-time environmental sensors, lightweight on-device ML classification (logistic regression, KNN), and a PID control loop to keep soil moisture in a target band. Includes a live dashboard, mobile alerts, and documented hardware build instructions.

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• Building meeting-mind — improving speaker diarisation accuracy and shipping richer Obsidian / Notion export workflows.
• Deepening Spring Boot and clean architecture patterns to round out my JVM backend toolkit.
• Exploring native macOS tooling and on-device AI workflows for productivity software.

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