Artemio Santiago Padilla Robles
I’m a Mexican engineer focused on building reliable ML infrastructure that improves large‑scale decision quality.
From atomic force microscopy to MLOps: I’ve scaled from nanometers to terabytes.
With a background in nanoscience research (STM/AFM/Raman spectroscopy) and a peer‑reviewed publication, I learned the discipline of working at 10⁻⁹ meters in ultra‑high vacuum. Today, I apply the same rigor to ML infrastructure—ensuring models scale reliably to 10⁹ requests.
The connection? Both demand obsessive care with signal‑to‑noise ratios, drift compensation, and reproducibility. Debugging a production model feels like aligning a scanning tunneling microscope—except the stakes are millions of financial decisions instead of a research publication.
At Círculo de Crédito, I’ve built ML infrastructure with scientific‑instrument‑grade reliability (99.99% uptime, sub‑second latency), while coordinating cross‑functional teams and external partners to translate advanced models into real business value. Because when models decide who gets credit, “good enough” isn’t enough.
- Patience forged from months refining a single spectrum
- Urgency of keeping production alive at scale
- Experience delivering infra where reliability is non‑negotiable
- Ability to align teams and stakeholders—internal and external—toward measurable impact
I also co‑founded SECiD at UNAM, fostering Mexico’s data science community with the same precision I once applied to spectrometers. I thrive in roles where ML infrastructure requires both microscopic attention to detail and macroscopic business impact—achieved not just through code, but by orchestrating people, processes, and technology.