I engineer ML systems at the mathematical level — abstraction is optional, understanding isn’t.
Role: Machine Learning Engineer
A production-grade neural network built entirely from scratch to master the mechanics of machine learning — not abstract them away. No frameworks. No shortcuts. I had full control over the math, the gradients, and the optimization pipeline.
This project was engineered to demonstrate deep mechanical understanding of neural network training dynamics, numerical stability, and optimization strategy. The architecture supports configurable multi-layer depth through a clean object-oriented interface, enabling controlled experimentation with initialization strategies, activation functions, and learning dynamics. The result is a lean, extensible ML system that reflects how I operate as an engineer: deconstruct complex abstractions, rebuild them under direct control, validate correctness end-to-end, and ship software that proves competence through execution.