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.
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 real-time dashboard to settle it: do physics-based forecasts, conventional ML, or DeepMind's state of the art actually win?
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).