Engineer first, builder always
AI Engineer · Data Engineer · Python Software Engineer — based in Denver, CO. I build production AI and data systems that turn slow, manual work into software people actually use.
How I got here
I came into software through data. I have a B.S. in Data Science from Arizona State, and I spent a lot of that time figuring out how to make messy information useful at scale — which is still the thread running through everything I build.
Most of my work has the same shape: there's a slow, manual process eating hours every week, and I get to replace it with something faster and more reliable. That's the part I enjoy — not AI for its own sake, but AI and data systems that remove real friction for real people.
I like to own things end to end, from the data and the model down to the API and the interface. The work I'm proudest of is the work that made it into production and changed how a team actually operates.
Caleb Kennedy · Denver, CO
What I'm building
I'm a NetSuite Data Engineer and AI/ML Engineer at Krayden, a B2B specialty-chemical distributor in Denver. I work across a two-year ERP migration to NetSuite while building the AI and data tooling around it.
- Product Intelligence Engine (RAG)
- LLM enrichment (42,000+ records)
- DBSCAN deduplication (20,000+ records)
- SQL ETL → executive Power BI
- ERP → NetSuite migration
Where I come from
B.S., Data Science
Arizona State University · Dec 2024
Data Science with an emphasis in Business Analysis / IT / Networking. For my capstone, I led a team of four building a Random Forest model that predicted violent protest events from 200,000+ records — reaching 73% accuracy and a 0.83 AUC.
Independent projects
Self-directed · ongoing
I keep building outside of work: PromptStudio, a full-stack platform for testing and evaluating LLM prompts, and PixelForge, a PyTorch image super-resolution model. It's how I learn new tools — by shipping something with them.
Boring problems, real impact
Why AI & data engineering
I'm drawn to the unglamorous, valuable problems: the catalog nobody wants to search by hand, the duplicate records nobody can reconcile, the report that takes all day to assemble. AI and data engineering are the tools — the point is impact you can measure.
How I work
I learn by building. I'd rather ship a rough first version and improve it than plan forever. I pay close attention to the people who'll actually use what I make, and I try to leave systems more reliable than I found them.
Where I want to go
I'm looking for roles where I can keep building production AI and data systems — as an AI/ML Engineer, Data Engineer, or Python Software Engineer — somewhere that values ownership and shipping real things.
Let's build something intelligent.
I'm open to AI/ML and data engineering roles and collaborations. If you have a problem that needs solving, I''d love to hear about it.