Software Engineer · AI Systems
Building AI technologies with AI tools.
I learned Computer Science first, then learned the inside of LLMs by hand. Now I build with both: the substrate underneath and the tools on top.
To understand how Transformers and GPTs actually work, I hand-coded them — following Karpathy's Neural Networks: Zero to Hero end-to-end — before I let AI tools touch my workflow at all. Once I understood the substrate, I started using AI for development gradually and deliberately.
To work the way real teams do, I held to professional practices — feature branches, pull requests that carry the reasoning, versioned releases, decisions documented as I made them.
That sequence matters. When an AI tool is wrong, I notice. When a model fails silently, I know how to diagnose it. The work below is built on that footing.
Selected work
Where models meet real engineering.
Engineering case studies. Each project links to a full technical write-up.
Rocket Engine Health Orchestrator
A rocket engine usually fails quietly first — vibration creeping up, a temperature trending warm, a pressure easing down, each still inside its own limit. Conventional redline monitoring is structurally blind to that. So I'm building an advisory layer that reads the channels together and flags the combined signature before any one of them breaks.
Turbofan Predictive Maintenance
I wanted real experience with what Transformers do beyond LLMs — so I picked a NASA problem where failure means someone dies: predicting a jet engine's remaining useful life.
AskMickey
I grew up going to Disney World and figured there had to be an easy, fun way to get any park info you wanted. Building it right meant putting a deterministic routing layer in front of the LLM — not just calling Gemini and hoping.
Weather Forecasting: Physics vs. ML
I've always loved meteorology — so I built a side-by-side comparison to put the claims to the test: physics-based GFS versus ML models from ECMWF AIFS and DeepMind WeatherNext, with a three-model accuracy scoreboard verified against real observations.
Pong from Pixels
A deep Q-network that learns Pong from nothing but the screen — no game state, just raw pixels — trained from scratch on a 6 GB consumer GPU. I'm hand-building the network, replay buffer, and training loop, and shipping the honest learning curve: the long flat stretch, the silent bugs, and the climb when it finally starts to see.
Toolkit
The whole stack, not just the top of it.
Foundations
- Neural Networks
- LLM Internals
- Transformer architecture
- Attention mechanisms
- Tokenization · BPE
- Backpropagation
- Karpathy — Zero to Hero
AI / ML
- PyTorch · Transformers
- Generative AI · LLMs
- RAG · Agentic AI
- Multi-Task Learning
- Prompt Engineering
- Structured Outputs
- Scikit-learn
Languages
- Python
- C · C++
- JavaScript
- Dart
- Java · Swift
- SQL
- HTML · CSS
Frameworks & Tools
- Flutter · Flask
- Pandas · NumPy
- Matplotlib
- Git
- Linux · UNIX
- Amazon EC2
- Arduino · Raspberry Pi
Experience
ML Software Engineer Intern
Epcom Corporation · Summers 2024–2025
Researched and built a Transformer-based predictive maintenance system for multivariate time-series sensor data. Integrated retrieval-augmented LLMs with maintenance records and failure-mode data using prompt engineering and structured outputs; built data pipelines and evaluation harnesses for iterative model development and benchmarking.
Software Development Intern
Epcom Corporation · Summers 2020–2023
Built full-stack components of an Applicant Tracking System — Linux · Apache · MySQL · PHP · JavaScript · Bootstrap, deployed on Amazon EC2. Wrote automated cron jobs with pattern recognition to ingest applicants from IMAP email.
Education
University of Florida
B.S. Computer Science · August 2025
St. Petersburg College
A.A., Cum Laude · GPA 3.7
Independent Coursework
Harvard CS50; MIT OpenCourseWare — Calculus, Differential Equations, Linear Algebra, Physics I–III, Artificial Intelligence (6.034); Stanford CS229 (Machine Learning).