Backend and full-stack engineer with a completed MSc in Artificial Intelligence & Machine Learning from University of Limerick. I build scalable systems, data pipelines, and intelligent products end-to-end.

• 🎓 Holds a completed MSc in Artificial Intelligence & Machine Learning from the University of Limerick, researching autoencoder-based anomaly detection with BDS Group (Lero)
• 🏥 Built the backend for one of the largest teleradiology platforms at 5C Network, real-time reporting for clinicians where reliability had direct patient impact
• 🔭 Previously SDE 2 (Backend) at BLKBOX.ai, an AI marketing platform processing 10M+ daily events
• ⚙️ Strong focus on backend architecture, machine learning, microservices, and data engineering
• 💡 Enjoy solving complex problems end-to-end, from raw data to a shipped product
• 🛠️ Experience across healthcare, fintech, and AI-driven marketing domains

Languages:       JavaScript, TypeScript, Python, SQL, C#
Frontend:        React, Next.js, React Native, Expo, Material UI, Tailwind CSS
Backend:         Node.js, Express, Flask, REST APIs, WebSockets
Databases:       PostgreSQL, MongoDB, DynamoDB, Redis, Firebase
AI/ML:           PyTorch, Pandas, NumPy, Scikit-learn, DEAP, CNNs, Autoencoders, MoE, Optuna, LLM prompt engineering, batch inference, OpenCV
Cloud and Infra: AWS (Lambda, SQS, S3, EC2, CloudWatch, SageMaker, IAM, Cognito), GCP, Heroku, Vercel, Docker, Kubernetes, GitHub Actions, Terraform
Tools:           Socket.IO, Puppeteer, Unity
Architecture:    Microservices, Monolithic

• ⚙️ Designed and deployed scalable backend systems and microservices in production, handling 10M+ daily events at 99.9% uptime
• 📊 Cut report load times by 96% (10s to 0.36s) through schema redesign and query optimisation on a real-time healthcare platform
• ⚡ Built an intelligent auto-assignment algorithm that cut clinical turnaround time by 25% in production
• 💰 Reduced server infrastructure costs by 30% and customer churn by 3% through systematic optimisation
• 🤝 Led and mentored a team of 3 engineers through architecture discussions and code review

Full-stack app pairing a genetic algorithm with an async AWS pipeline (SageMaker, SQS, Lambda) to generate cocktail recommendations. Batch inference design cut SageMaker calls by 99.9% (150K to 150 per run).

RapidMock accelerates frontend development by mocking endpoints with real responses like 200, 404, 500, and many more, all with a single click and no change in frontend code.

Ablation study classifying 300K Google QuickDraw sketches across 30 classes, reaching 87.88% accuracy pretrained and 83.84% from scratch with zero overfitting.

Built a genetic programming classifier from scratch to predict >$50k income, achieving over 78% accuracy and ranking 4th on the leaderboard.

• Backend Engineer roles
• Full-Stack Engineer roles
• AI/ML Engineer roles

Based in Dublin. Authorized to work in Ireland. Available immediately.

Always open to building and discussing new ideas, software development, or market validation. Just drop a mail and we can schedule a call.