About
I’m an applied AI and data engineer in Seattle. Most of what I build now is meant to be used: school software for Tanzanian institutions, a safari operator’s back office, a basketball analytics platform that has to survive a season.
Before that I spent three years at LTIMindtree building enterprise data platforms on Azure for Fortune 500 clients, and led technical delivery for a thirty-person Data & AI team. In the summer of 2025 I built a procurement agent for a marine logistics company: retrieval over their own policy documents, structured outputs, and a human approval step before anything left the system. I want more of that combination: data platforms underneath, governed AI on top, and someone depending on the result.
I grew up in a small village called Ngyeku, in the Arusha region of northern Tanzania. The path from there to this work has been shaped, almost entirely, by people who chose to invest in me when they didn’t have to. The course of my life changed when I was twelve, when an education-focused NGO called The Foundation For Tomorrow (TFFT) found me and gave me access to schools, teachers, and resources I never would have reached otherwise. In high school, a teacher named Michael Sarungi sat me in front of a computer for the first time and introduced me to programming. I came to the US in 2017 to continue my studies.
For my undergraduate and master’s degrees, I’ve been supported by my incredibly generous American family, who have stood beside me throughout my education and life since I arrived in 2017 and continue to do so. I count myself uncommonly lucky: I have two families now, one in Tanzania and one in the US, and the person I’ve become has been shaped by both.
Highlights
How I work
Shule Fanisi looked like a records system until I got into the actual rules: every school grades on its own scale, fees are structured differently at every level, and the reports the government expects do not map cleanly onto any of it. The software got simpler once I stopped guessing at those rules and went and found them.
I would rather find out I am wrong early, though I am not always quick about it. My March Madness site advertised 79.6% accuracy for most of a season before I worked out that three of its features were broken, two of them leaking. The honest number was closer to 71%, which put the model behind the plain Elo rating sitting inside it. I deleted the model, published the estimated ceiling for the sport next to the new numbers, and wrote up how I had walked past the same mistake twice.
At LTIMindtree I went from junior engineer to leading a team of about thirty, and the mentoring is the part I still think about. Several of the engineers I worked with moved into leadership roles afterwards. Before that I tutored algebra and calculus at Seattle Central College, with students from all over the world who were finding it hard going. I learned more about explaining a system in those two jobs than anywhere since.
What I bring
Nearly three years at LTIMindtree, where I was promoted from Junior to Senior Data Engineer and built enterprise analytics infrastructure on Microsoft Azure for Fortune 500 clients. A summer at a marine logistics company building an AI procurement agent. An MS in Data Science from the University of Washington (March 2026, 3.91 GPA). Alongside most of it, my own things: Shule Fanisi, school software for Tanzanian institutions, Safari King Africa, and Ubunifu Madness, a college basketball analytics platform that runs through the season.
The place I have learned to look hardest is wherever one thing hands off to another: data crossing a boundary, one service calling the next, a person typing into a form. Most of the failures I have had to debug started at one of those joins.
What I'm looking for
Right now I'm looking for work in data engineering or AI engineering, ideally somewhere close to the people using what I build. Seattle is home, though I would move for the right role.
I also work with teams now and then on AI and software problems. If you're building something and think I could help, let's talk.
Outside work
Seattle has reeled me in for good. Seahawks first, Mariners with patience, Manchester United out of long habit, and enough college basketball that it turned into a side project. When I am not at a screen I am usually on a bike or out running, which mostly gives the codebase a chance to think without me.