Where does AI actually help?
Understanding the workflow before choosing the technology. Finding the places where an assistant can help people search, reason, and get work done.
Agent workflows · Retrieval · Customer discovery
A customer perspective. An architect’s curiosity.
Nathan
Redmond.
Applied AI & Cloud Architecture
Customer Engineer at Google Cloud Troy, Ohio
I work with customers to turn AI ideas into practical architectures—with attention to evaluation, security, and the people who will operate them.
Government & higher educationGoogle Cloud · Previously AWSU.S. Marine veteran
01 / Background
A career on both sides
of the conversation.
Before advising organizations on the cloud, I worked inside them—leading IT for a school district and in higher education.
That experience shapes the questions I bring to customer conversations today: who will use this, who will support it, and what will make it useful in their day-to-day work?
2020 — Present
Google Cloud
Customer Engineer · Public Sector
Working with government and education customers on cloud architecture and applied AI—from understanding the problem to exploring a practical technical approach.
2018 — 2020
Amazon Web Services
Senior Solutions Architect
Helping public-sector customers make cloud architecture decisions, plan migrations, and understand the tradeoffs behind them.
2016 — 2018
University of South Carolina
Infrastructure Architect Manager · Previously Director of Technology, Housing
University IT leadership, infrastructure architecture, and cloud planning. A view of technology from inside the organization it serves.
2004 — 2016
Allen East Local Schools
IT Director
Leading school district technology, with responsibility for the infrastructure that students, teachers, and staff rely on.
02 / Current focus
The questions that
keep me curious.
My focus is applied AI for government and higher education. I’m especially interested in what makes an idea useful, a system dependable, and a handoff successful.
How do we know it works?
Exploring evaluations that expose failure modes, traces that explain behavior, and human review at the decisions that matter.
Evaluation · Observability · Human approval
What happens after the demo?
Thinking through access, privacy, operational ownership, and the practical work of putting an AI system in someone else’s hands.
Security · Governance · Production handoff
03 / Outside work
Ohio is home.
Faith, family, and a little room to build things.
I’m a husband, father of three, and U.S. Marine veteran. Home is Troy, Ohio, where you’ll often find me working on a project around our property or tending something on the smoker.
I like work that leaves something useful behind. That holds true away from a keyboard, too.
04 / A conversation
Let’s compare notes.
Have an interesting problem at the intersection of customers, cloud, and AI?
I welcome conversations about technical collaboration, speaking, and opportunities where this experience can make a difference.