I'm a petroleum engineer who built an AI enablement platform at a major E&P operator and trained 250+ people on Claude Code — from individual contributors to the COO. Four engineers each found $1M+ in savings. One prevented a loss of primary containment event. I teach the same workflows externally.
Petroleum engineer by training. I've spent 14 years across production engineering, SCADA systems, and enterprise software — at Chevron, Xcel Energy, and a major upstream E&P operator. I build tools that engineers actually use.
B.S. Petroleum Engineering from Colorado School of Mines. Started in economic modeling and well abandonment at Chevron, moved through SCADA systems at Xcel Energy (gas pipelines and electric transmission), and landed in production systems engineering where I build data-driven tools, ML systems, and cloud infrastructure for upstream operations.
In early 2026 I built a company-wide AI enablement platform from scratch: MCP infrastructure for database access, a provisioning portal, a self-service Heroku-style deployment platform, and a training program that put AI coding tools in front of 250+ engineers. All organic demand.
Practical, hands-on training for engineers who want to use AI coding tools on real engineering problems. Not a lecture. Not "intro to AI." You bring your data, we solve your problems live.
Most AI training is taught by people who have never run a decline curve, read a SCADA alarm, or argued with a vendor about polling rates. I have. The workflows I teach are the same ones that produced $4M+ in savings at a major E&P operator — adapted for your team's data, your systems, and your engineering problems.
First pilot cohort is in the works. To get notified when registration opens, reach out at the email below or connect on LinkedIn.
I build across the full stack — from iOS apps with custom ML models to enterprise Kubernetes platforms. Here's a sample.
AR plane spotting app. SwiftUI, C# WebSocket broadcast service with physics engine, Django/PostGIS backend, custom YOLOX and D-FINE ML models trained from scratch, ADS-B data pipeline, GitLab CI/CD.
Precision NTP timing on Raspberry Pi with GPS PPS. Thermal management, stability analysis, and the engineering behind sub-microsecond accuracy.
PID-controlled autopilot for X-Plane built in Python. Real-time control loop via Flask, Redis, and WebSockets. Full-stack systems engineering.
Reverse-engineered the SMBus protocol for Inventus 24V LiFePO4 batteries with no public documentation. Full register map, per-cell voltages, health profiling across a 5-pack battery bank.
Restaurant matching app. .NET 8 Web API, PostgreSQL, SwiftUI iOS app, Django website, Apify data pipeline, OneSignal push notifications.
Dell PowerEdge R630 in a south Denver data center. 18-core Xeon, 128GB RAM, 3.4TB all-SSD ZFS. Runs 17 VMs and 16 containers for ~$55/month. Equivalent AWS cost: ~$2,000/month.
More at austinsnerdythings.com — 12+ pages of technical content spanning ML/CV, DevOps, embedded systems, IoT, SCADA, storage engineering, and network architecture.
Interested in training for your team? Have a question? Reach out directly.
[email protected]