How I got here
I didn’t set out to contribute to the Linux kernel or to open-source mapping tools — I got there by following problems I actually had. My mouse didn’t work on Linux, so I read enough of the HID subsystem to fix it and sent the patch upstream. I wanted to buy a used car, so I turned the search into a dataset. I wanted to understand genetic algorithms, so I built one that escapes a room on its own. That pattern — find a real question, build my way to the answer, share the result — is how I’ve taught myself most of what I know.What I’m like to work with
Curious and self-directed
I pick things up quickly and don’t need hand-holding to get productive.
Comfortable across the stack
From C at the driver level to web apps and data pipelines — I go where the problem is.
Community-minded
I do a lot of my work in the open and enjoy collaborating with people who care about the craft.
Pragmatic
I care about shipping things that work and are actually useful, not just clever.
Where I’m headed
Data science is where my engineering instincts and my curiosity meet. Day to day I work on AI systems — Retrieval-Augmented Generation in particular — where the challenge is getting large, messy knowledge bases to actually inform what a model says. Before that I spent years close to data: ETL pipelines, business intelligence, visualization in R and Tableau. I want to keep going deeper on applied AI and data science, building systems that turn real-world data into decisions, on top of the software-engineering foundation I already have.What I’m looking for
I’m open to data science, applied-AI, and software engineering roles — remote, or based in Costa Rica. I’m especially drawn to work involving AI and RAG systems, data engineering, and open source, on a team that values learning and building in the open.If you think there’s a fit, I’d love to hear from you: contact@miltonials.com.