I'm an MS Business Analytics candidate at DePaul University's Kellstadt Graduate School of Business (GPA 3.925, graduating June 2026), with hands-on experience from a Data Analytics internship at Unified Mentor. I'm deliberately retraining toward Machine Learning Engineering — I completed the Stanford Machine Learning Specialization and the DeepLearning.AI Deep Learning Specialization, and instead of stopping at the certificate, I immediately rebuilt each concept into a shipped project: classical ML into a 145-class NLP classifier, agent orchestration into a self-hosted multi-agent platform with 293 passing tests.
Today I work across the full stack end-to-end: SQL and Python for the data layer, scikit-learn/XGBoost/TensorFlow and LLM agents for the intelligence layer, Power BI/Tableau for the story, and Node.js, FastAPI, Docker, and Nginx when the answer is "build and deploy the product." Outside coursework, I ship independent projects to real infrastructure — a published PyPI package with a full test suite, a production chat app serving real users, and a self-hosted AI agent platform — because the fastest way to prove I can do the job, and the fastest way to actually learn it, is to already be doing it.
I'm not finished learning, and I don't plan to be — every project I take on picks up at least one tool or technique I haven't used in production yet. I'm looking for a Machine Learning / AI Engineering role (or Data Analytics / BI role with a growth path into one) on a team willing to invest in that trajectory — I learn fast, I ship what I learn, and I'd rather be evaluated on that pattern than on years of titled experience.
