México — AI Lab
Hi. I’m Estefanía León.
AI Engineer.
Building production LLM systems and AI architecture at Natura & Co, researching representation learning for collider data during a research residency at CERN openlab, and pursuing a PhD in Quantum AI and Optimization.
- Machine Learning/
- LLMOps/
- AI Architecture/
- Research/
- Quantum Computing
A designer’s eye, an engineer’s hands.
- 01Game Design
- 02Software
- 03Machine Learning
- 04LLMOps
- 05PhD, in progress
I didn’t arrive at machine learning through a straight line. I started with a degree in video game design and interactive content — learning how structure, feedback, and interface decisions decide whether a system actually works for the person using it. That instinct for structure is what later pulled me toward representation learning: wanting to see the actual shape of things, not just their surface.
The bridge was research. While building web and VR projects, I was also analyzing CERN ALICE open data and training deep learning models to classify particle interactions — my first real exposure to a field that holds models to a standard most industry work never has to meet. Two master’s degrees later (IoT & AI, then Big Data), that research habit turned into a full engineering discipline.
Today I’m an LLMOps Engineer at Natura & Co, designing production conversational AI systems and the architecture that keeps them fast and affordable at scale. In parallel, I’m a research student with CERN openlab’s Next Generation Triggers initiative — a focused, two-month residency, not the whole story — and I’m pursuing a PhD in Quantum AI and Optimization. Outside of all that: Hyrox training, and finding reasons to travel.
A career built out of order, on purpose.
Every stop taught the next one something. The timeline below is chronological — the only place on this site where numbering carries meaning.
- 2021
B.S. Video Game Design & Interactive Content
Universidad Kino
Started in interactive and visual design — learning how structure, feedback, and interface decisions shape whether a system is actually usable. That instinct for structure carried straight into machine learning later on.
DesignInteractive SystemsC# - 2022–2024
Scientific Researcher & Web/VR Developer
UNISON · CONACYT (concurrent)
Analyzed ALICE experiment open data and built ML/deep learning models for hadronic particle detection and classification at UNISON, while building responsive government websites and a VR laboratory experience for CONACYT.
Machine LearningALICE Open DataVRWeb Development - 2024
M.S. Engineering in IoT and Artificial Intelligence
Universidad de Sonora
Deepened the applied ML foundation — moving from research scripts to systems: model deployment, embedded/IoT constraints, and production-minded engineering.
M.S.IoTApplied AI - 2025
LLMOps Engineer
Natura & Co
Designing and shipping production conversational AI systems, leading token-usage and inference-cost reduction through prompt redesign, caching, and model-routing, and contributing to scalable AI architecture and workflow automation.
LLMOpsAI ArchitectureCost Optimization - 2026
Research Student — Next Generation Triggers
CERN openlab (research residency)
A focused, two-month research residency studying representation learning on LHC collider data — what physically meaningful information survives the L1 trigger relative to full particle-level data — building autoencoder and Linformer-based architectures for interpretable collision-event embeddings.
Research ResidencyRepresentation LearningParticle Physics - 2026 ·
M.S. Big Data
Universidad Autónoma de Guadalajara
Rounding out the data and ML foundation with large-scale data engineering and analytics — the infrastructure side of building systems that hold up outside a notebook.
M.S.Big DataData Engineering - Ahead
PhD, in progress
Quantum AI and Optimization
Pursuing doctoral research at the intersection of quantum computing, machine learning, and optimization — the next layer of the same question: how do we build systems that represent and solve problems well.
ResearchQuantum ComputingOptimization
What I actually spend my time thinking about.
Six threads that keep reappearing across projects, papers, and late-night reading. Open one to go deeper.
Selected work, end to end.
Each project page walks through the problem, the approach, and what actually happened when it shipped.
On explaining this stuff out loud.
Conversations about the path from design to AI, and about what it's like building ML for a physics experiment.