Skills

Web

I have professional experience engineering full-stack web applications and interactive websites. I've built modern backends with Node and Bun, as well as specialized sites with PHP and custom WordPress plugins. I've led full-scale, from-scratch overhauls of enterprise websites, built production grade frontends with React as well as vanilla HTML, CSS, and JavaScript, managed business critical production deployments, and built integrated web experiences featuring real-time data processing with WebSockets and libraries like Three.js. On the systems side, I've architected CI/CD pipelines with GitHub Actions and Docker, deployed secure multi-tenant serverless applications on AWS (Lambda, DynamoDB, Cognito), engineered backend platform systems including an OAuth 2.0/OIDC authorization server with PKCE and token introspection, integrated usage-based billing and payment systems, and hardened production infrastructure to safely run untrusted workloads with process isolation and network egress controls.

iOS and visionOS

I’ve worked extensively on iOS and visionOS projects, including advanced AR/VR development using RealityKit. I have experience with RealityKit’s entity-component-system architecture, coordinating SharePlay in 3D spaces, designing and debugging complex and highly customized view hierarchies in UIKit and SwiftUI, package management in Xcode, concurrency in Swift 6, and building ultra high performance, heapless programs. I’m comfortable across Apple's full development stack, from optimizing performance with Instruments, managing scenes in Reality Composer Pro, deploying CI/CD pipelines with Xcode Cloud and TestFlight, and training/using ML models with Create and Core ML.

ML and RL

Professionally, I've applied machine learning to both enterprise and government systems. I designed and built a real time computer vision pipeline that trains models capable of recognizing and tracking the orientation of 3D objects using LIDAR. I’ve also fine-tuned and deployed foundation models on H100 clusters to improve performance on proprietary domain-specific tasks. I've also built multimodal agentic pipelines that generate structured metadata, using LLM juries for consensus scoring and evaluation driven loops that iteratively optimize prompts.

Personally, my interest in ML extends much further. I built a transformer using only numpy, and have worked directly with embeddings, autoencoders, and more. I've had some exposure to deep reinforcement learning and loved it. I've read Reinforcement Learning: An Introduction by Sutton and Barto cover to cover and implemented some of the core algorithms: TD(λ), DQN and SARSA varieties, some policy gradients, off policy importance sampling, etc.