GOODYEAR, AZ · REMOTE (US)
HYBRID PHOENIX
Jordan Truong — AI Engineer in Goodyear, AZ. Open to remote-US or Phoenix-area roles.
Agentic engineering — formal specs, CI/CD gates, eval-gated reliability. Not vibe coding.
Production agentic AI: multi-agent systems, RAG pipelines, and the infrastructure beneath them. Solo-built and run in daily production, end to end, from a terminal in Arizona.
The arc: twelve years a salon owner-operator. Then roughly four years in enterprise IT. Now AI engineering, shipping software that runs itself. Same instinct throughout: make the hard thing dependable.
Also live and provable, found in tonight's audit: /ops — Langfuse-traced cost dashboard with per-request pricing · Anthropic Academy ×15, Claude Partner — Claude Code, and Google AI Professional Certificate.
Solo-built multi-agent VPS, SoloInvoice SaaS, RCM knowledge assistant. All in production.
Built production AI tooling across two enterprise campaigns: HIPAA RCM copilot (every scenario audited, 72 KB-grounded scripts, 27 RPM playbooks, 41 canned-note codes) and ACES Aid (162 flows, floor-wide adoption, 1,500+ indexed keywords). Both in daily floor use.
Managed provisioning across 1,000+ franchise locations. Led network/security audits with firmware rollouts sequenced across timezone waves.
Two enterprise campaigns: HMH education software (app/account triage, escalation) and Dell Technologies (hardware diagnostics via Zendesk, live Teams collaboration with senior techs, warranty logistics).
Twelve years running a service business end-to-end: hiring, payroll, scheduling, P&L. Operational systems thinking at its most direct.
Open-source MCP memory server. One command to install: pip install all41n14lla. A 90-test suite runs green in CI across Python 3.11 through 3.14.
Ten agentic systems in daily production: healthcare + telecom copilots (every scenario audited, 162 flows), five in-browser demos — RAG, SQL, email triage, agent orchestration, semantic layer — SaaS with offline licensing, a 7-agent outreach engine, and the platform wiring all of it. Tap to browse.
Fifty-plus skills loaded on demand, fifteen MCP servers, auto-memory across sessions, and layered guardrails. The model is 10% of the system — the harness is the other 90%.
Scout finds. Diagnoser maps. Builder ships. Filmer cuts. Checker validates. Pitcher sends. No staff. No retainer. $0.10/lead.
Type a question in plain English. Watch it become a safe, parameterized query.
Open demo →Paste raw email. The agent classifies, prioritizes, and drafts a reply in one pass.
Open demo →Inline citations, confidence-gated abstention — it declines rather than guesses. Domain: healthcare revenue cycle.
Open demo →A three-role pipeline over real retrieved facts. The Critic checks the draft against the facts and forces a revision round when it finds an unsupported claim — capped at 2 rounds, never an infinite loop.
Open demo →Ask a business question. The model can only pick a metric off a fixed, published catalog — it never writes its own SQL. Off-catalog questions get refused, not guessed.
Open demo →Or open the chat bubble (bottom-right) to ask me anything.
Every demo ships through typecheck, eval, and prompt-regression gates on every commit — and the guardrails are visible live, not just claimed: RAG declines instead of guessing, the Critic rejects unsupported claims, the semantic layer refuses off-catalog metrics.
Fine-tuned Qwen2.5-0.5B locally on 8 examples from this site's own content, testing whether it could drop the ~190-line system prompt the live chatbot runs on. It couldn't. Real transcript from the eval run:
Base model + system prompt — correct: "An open-source MCP memory server for AI agents, maintained by Jordan Truong, published on PyPI and GitHub."
Fine-tuned, no prompt — hallucinated: "...a web application for managing all 41 million users of the Netflix app..."
Owner-operator of a salon and beauty business. Scheduling, payroll, inventory, hiring, client experience — all of it. The job taught systems thinking before I had a name for it: every constraint is a design problem, every bottleneck is a process failure waiting to be solved.
Joined the workforce-management tech stack at scale. Claims processing, healthcare RCM, multi-team ops coordination. Learned what large-system reliability actually means when a misconfigured rule costs someone a reimbursement. Then LLMs became capable enough to matter.
Shipped a PyPI MCP server, a live RAG+GraphRAG knowledge assistant, a multi-agent VPS brain, and a licensed SaaS product — all solo, all in production. The plan is to keep shipping until the systems are earning while I sleep.
One diagram, drawn from the code the same day this was written — not a slide. Two entry points, one shared trust boundary, three honesty tiers on every demo answer.
What this system is NOT: no PHI, no real user accounts, no payment data. A HIPAA-safe tool built at Valor is a separate system — not this one.
Five decisions from the systems above, with the cost attached. Anyone can list what they shipped — these are the choices that had a price, including the ones that cost me something.
Remote-US or Phoenix metro only. Open to full-time AI / agentic-systems roles.