BANKING · PRODUCTION EXPERIENCE
AI for analyst workflows
Technical ownership across AML review, document intelligence, and M&A due diligence at Sunwest Bank. Built around evaluation, traceable outputs, and human judgment.
Read the case study →Tyler Alika Gee
I build AI systems and explore how they work.
My work spans banking, clinical research, and local AI. Here are a few things I’ve been building and learning.
Selected work
OPEN SOURCE · RUNTIME RESEARCH
Strata: memory-aware inference on Apple Silicon
An LLM engine and learning laboratory for memory budgeting, weight residency, and evidence-based backend selection.
Read the case study →timeline
2025 - Present
AVP, AI/ML Engineer at Sunwest Bank (October 2025 – Present). Technical lead for enterprise AI across lending, underwriting, M&A, and compliance. Builds document intelligence, retrieval and agentic systems, LLM fine-tuning and evaluation, and production monitoring and AI governance.
2024 - 2025
Data Scientist / AI Engineer at Datafy in Ogden, Utah (August 2024 – May 2025). Built anomaly detection over geolocation and event data, production data pipelines, and MLOps workflows. Used analyst feedback to improve forecasting models.
2018 - 2025
Data Scientist / AI Researcher at the University of Utah (August 2018 – October 2025), part-time across academic terms. Led AI work on medical diagnostics with UCSF, using breath and electrochemical sensor data. Built clinical data pipelines, synthetic patient data models, and NVIDIA Jetson edge deployments.
2022
Data Scientist/AI Intern at Micron Technology (Boise). Built visualization tooling, manufacturing analytics dashboards, and ML pipelines for metrology prediction.
2021
Process Engineer Intern at Marathon Petroleum in Salt Lake City, applying process optimization work to refinery operations and throughput economics.
education
B.S. Chemical Engineering, University of Utah.
A.S., Weber State University.
bio
Tyler Alika Gee is an AI and data scientist who builds useful software at the intersection of machine learning, automation, and web products. His work spans finance, healthcare, semiconductor manufacturing, cultural projects, and edge AI. He is most interested in systems that make complex data easier to act on.
blog
Notes on building AI systems, local inference, and the lessons behind my projects.
highlights
- Led AI work on medical diagnosis research using breath-sample data and ML/CNN methods.
- Built MLOps pipelines for iterative retraining and deployed ML classification workflows to Linux-based NVIDIA Jetson edge devices.
- Built AI tooling across SQL, pandas, Django, Tableau, and cloud systems (AWS + Azure), with production-oriented data pipelines.
featured talks
- Guest Lecturer - David Eccles School of Business, University of Utah.
publications
- GC-MS and electro-chemical breath-data classification research paper (in progress).