AI Product & Operations
Six years building platforms and operational processes at MX, scaled into AI systems used across the company — and teaching 190+ product managers to ship AI-native work of their own.
01 — Story
Salt Lake City metro · remote or open to relocation
I started on the support desk at MX, translating fintech customers’ patterns and escalations into input for Product. That seat taught me the part of the job most roadmaps skip: what breaks for real users, and why.
In 2019 I identified a monitoring gap and built the role to fill it — designing MX’s company-wide monitoring and alerting infrastructure from scratch, writing the SLOs and SLIs behind it, running postmortems with engineers, and producing client-facing RCAs. The role eventually grew into a dedicated SRE function. The status-page platform I drove from concept to company-wide adoption cut related support calls 90%.
Since 2024 I’ve carried that build-and-deploy background into AI: shipping production agents like TechOps Co-Pilot, authoring the lifecycle process that gates AI behavior into production, and building the adoption frameworks that turned ad-hoc AI experiments into a measured, governed function.
Outside of work I write, record, and produce my own music as Evening with Crows — same instinct, different medium — and I’m usually cold plunging, skating, or at a show.
2017
Software Support Specialist, MX
Front line of fintech support; voice-of-customer input to Product.
2019
Platform Monitoring Technician, MX
Built company-wide monitoring & alerting from scratch; SLOs, RCAs, status page.
2020
B.S. Marketing, Utah Valley University
Minor in Business Management.
2024
Product Support Engineer | AI Operations, MX
Production AI agents, governance gates, adoption frameworks.
2026
Capstone Lead, AI Product Academy
Guided 190+ PMs through building and launching AI-native prototypes.
02 — Vision & values
AI earns trust in production, not in demos. The future I want to help build is one where companies ship AI people actually rely on — measured against real work, gated by real standards, and honest about what it can’t do. Most organizations don’t need another chatbot; they need the platforms, processes, and proof that let good AI survive contact with operations. That’s the infrastructure I like building.
Evidence over adjectives
If a claim can’t survive an interview question, it doesn’t ship.
Production is the milestone
A demo is a hypothesis. Adoption is the result.
Governance is a feature
The gate that approves AI behavior is what makes AI worth trusting.
Teach what you learn
Frameworks that only work for their author aren’t frameworks.
Cost is a design constraint
The right model for the task, not the biggest one available.
Useful beats impressive
The best AI work disappears into someone’s easier day.
03 — Selected work
Production systems and platforms — what each one was for, how it was built, and what it changed. Case studies are being written up; published ones open in full.
A prototype built from scratch in two weeks, targeting a 50% cut in client goal live times and shifting Solution Architects from a sales posture into a partnership.
A shareable vision for an agentic AI platform at MX — researched from how the industry is solving it, iterated until the buildable shape was clear, and turned into a shareable artifact.
The gate process that moves a customer-facing AI behavior from build to production — alpha readiness, controlled Support beta, feedback scorecard, and a multi-condition exit — piloted on the MFA Loop Triager.
A branded capstone gallery I built for the AI PM Bootcamp so incoming students could see what prior cohorts shipped before starting their own project — one of 3 core capstone resources built across 3 cohorts.
04 — AI assets
The AI systems I've built or run: agents, governance processes, prompt systems, and knowledge tools. Some are production systems at work; some are personal experiments. Screenshots and demos land here as they're cleared to share.
A zero-setup personal AI knowledge system that onboards itself, scaffolds a custom capture-and-retrieval pipeline tuned per user, and uses cost-aware model routing.
A meta-agent that designs, structures, and audits production-grade Atlassian Rovo agents for non-technical leaders — enabling rapid deployment in minutes while strictly enforcing platform governance.
An autonomous compliance agent built with Rovo Architect that independently investigates third-party AI vendors across internal tickets, policies, DPAs, and web docs to generate evidence-backed risk scores and publish-ready Confluence assessments in minutes.
A Rovo agent that turns a rough idea into a template-compliant Jira Epic, Story, or Task — sizing rules and RICE scoring built into the UX — cutting ticket-creation time 80% (30+ min to under 5 min) with 100% template compliance by design.
A Rovo agent that automatically classifies every new ticket the moment it enters the Product Support queue in Jira Service Management (JSM) through a deterministic three-stage decision framework, returning strict JSON that JSM Automation maps into routing and telemetry fields — triaging 100% of tickets with the proper labels to speed up troubleshooting.
05 — How I think & work
A discovery-to-deployment loop for shipping AI safely: Specify the problem, Harvest the data, Inspect the current process, Feed & Test the system, and set Telemetry before anything goes live.
A staged model that standardizes AI discovery, evaluation, and operational rollout across teams — so adoption is a designed outcome, not a hope.
I separate who builds an AI behavior from who approves it for production. Having done both, I design gates that builders don’t route around.
06 — Opportunities
AI Product Manager, AI Operations, and AI Enablement roles — especially teams that need someone to take AI from promising experiments to governed, adopted, measured systems. Remote-first; open to relocation for the right role.
The fastest next step is an email. I reply quickly.
07 — Contact
Roles, projects, collaborations — if it involves getting AI out of the demo and into production, I want to hear about it.