AI Engineer

James Phoenix

Building production AI systems and SaaS applications.

I build AI-powered SaaS products from the ground up. My most recent project is AI Rank Tracker, a production system with ~350K LOC that monitors brand visibility across ChatGPT, Claude, and Gemini.

Along the way, I wrote an O'Reilly book on prompt engineering and have taught 306K+ developers taught through Udemy courses and 45+ bootcamps at General Assembly.

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Numbers That Tell the Story

Real metrics from real systems

~350K

latest project code size

74,896

Test Code LOC

347

DB Migrations

917

RPS Throughput

Prod Dashboards

Alert Policies

What I'm Building Now

AI Rank Tracker

Tracks brand visibility across ChatGPT, Claude, Gemini, and Perplexity. Currently processing 5K queries daily with capacity to scale to 100K. Built with a 3-stage pipeline handling complex orchestration and rate limiting.

Visit BrandVisibility.ai →

O'Reilly Book

"Prompt Engineering for Generative AI" started as my notes for teaching LLMs. 5,000+ copies sold with a 4.4-star rating. It covers practical techniques that work in production, not just demos.

View on Amazon →

General Assembly

Five years and 45+ bootcamps have taught me one thing: everyone can code, they just need the right guide. I created GA's first Applied AI Engineering curriculum because the industry needed practitioners, not just theorists.

5+ years shaping the next generation

Technologies

Battle-tested stack

From frontend to infrastructure, every tool chosen for production reliability. These aren't just technologies I know. They're technologies I've deployed, scaled, and debugged at 3am.

Full-stack TypeScript

End-to-end type safety from React to API

Production observability

Traces, metrics, and logs with OpenTelemetry

Infrastructure as code

Reproducible deployments with Terraform