AI Platform Architect · Enterprise AI · Systems Architecture

Texas Triangle · Email · LinkedIn · X

I design production AI systems where model capability, human authority, infrastructure, and business risk meet.

My work focuses on governed agentic systems, AI-ready data platforms, cloud infrastructure, and architectures that make decisions observable, verifiable, and operationally safe.

Clarity over cleverness. Correctness over speed. Evidence over hype.

Current: Founder, Apex AI|ML Engineering
Prior: Minotaur Consulting · Investment Analysis / Office of the CIO · Wall Street

I build around real operating constraints: authority, evidence, failure paths, infrastructure, and business consequence.

Governed AI agents on AWS — proving that capability, identity, and authority are not the same thing.

Agent Foundry is a production-oriented governance system for autonomous AI agents, built in Python and validated against real AWS infrastructure.

A governed AI trust boundary for systems where agents may recommend actions but cannot independently authorize high-risk execution.

The research seat can propose a trade. It cannot send one.

I was a trader. I did not build agents that trade. I built the desk that will not let them.

AI proposes → human authorizes → system verifies current evidence → execution

A SOC evidence architecture mapped to the NIST AI RMF, designed so agent actions remain traceable, reviewable, and governable.

A supply-chain risk and scenario-planning platform built for operational decision support under uncertainty.

A workstation is infrastructure.

This project treats the AI engineering environment as a governed, reproducible operational system rather than a developer laptop.

Languages: Python · SQL · Bash
Systems: Linux · Rocky Linux · Ubuntu · RHEL
Cloud & Infrastructure: AWS · GCP · Terraform · Docker · Kubernetes · PostgreSQL
AI: LLMs · RAG · Agents · Structured Outputs · Tool Calling · Evaluation · Human-in-the-Loop Controls
Domains: Energy & Commodities · Supply Chain · Financial Services · Data Platforms · AI Infrastructure

Engineering principle: Standard library first. Add a dependency when the cost of building exceeds the cost of owning it.

Technical capability matters. Judgment matters more.

I look for the decision underneath the technology:

• What problem are we actually solving?
• What authority should the AI have?
• What evidence must be true before action?
• What happens when the model is wrong?
• How will we observe, verify, and recover?
• What business consequence does the architecture create?

AI transformation is not only a technology problem. It changes workflows, decisions, accountability, and operating models.

I write about technology, cybersecurity, risk, and AI at Cyber Essentials.

I also build analytical models and dashboards that turn complex technical, economic, and business systems into decision-ready information.

An interactive analytical model exploring what happens when capital allocation begins to behave like an intelligent system.

The model maps the convergence of AI capability, infrastructure, energy demand, investment, and orbital systems across major technology, cloud, aerospace, and energy organizations.

The visualization is only the presentation layer.

An embedded information layer exposes the methodology behind each classification—including AI integration level, cognitive value layer, investment, energy footprint, and the decision logic used to derive the result.

A viewer can move from the conclusion directly into the reasoning behind it:

Company → Cognitive Value Layer → Orbit Tier → Destination Node

The underlying model distinguishes between organizations generating High Cognitive ROI—where AI, cloud, and compute increasingly convert infrastructure into intelligence—and organizations creating Operational Value, where energy, aerospace, and physical systems provide the capacity that intelligence depends on.

Both are essential.

One builds. One thinks.

The conclusion should never stand alone. The evidence and methodology required to evaluate it should travel with it.

That means the viewer is not simply asked to trust a classification. The analytical logic, thresholds, metrics, and assumptions remain available for inspection inside the experience.

Energy once powered infrastructure.

Increasingly, intelligence determines how infrastructure—and ultimately capital—is deployed.

As AI capability becomes embedded deeper into physical infrastructure, the question shifts from:

Where is capital being spent?

to:

Where is capital learning to think?

Not by power. Not by orbit. But by cognition.

• RAG Attack Surface — mapping how vulnerabilities propagate across retrieval-augmented AI systems
• Infrastructure / FinOps — connecting architecture decisions to cost, risk, and investment consequence
• Energy / Commodities — decision intelligence across operational and market systems

Explore the full Tableau portfolio →

Senior roles across:

AI Architecture · Enterprise AI · AI Platforms · Cloud / AI Architecture · AI Transformation Consulting · Technical Chief of Staff

Geographic focus: Austin, TX; open to Texas Triangle

Work style: In-office or hybrid

Domain interest: Agentic AI architecture for regulated, data-heavy industries.

Open to meaningful travel.

Senior. Technical. Strategic. Evidence-driven.

Close enough to the work to build and reason. Senior enough to shape the direction.

^{Updated September 2026}