Everyone seems to be hiring AI engineers—but what does that actually mean?

Using Claude Code, Cursor, or ChatGPT doesn't automatically make someone an AI engineer. So where's the line between software engineering with AI and engineering AI systems?

This week we're joined by Byron McKay, Director of Learning at Gauntlet AI, to discuss how they train engineers for AI roles, what companies are actually looking for, and why the most important AI engineering skills have surprisingly little to do with prompting.

We explore why system design has become essential, why RAG is far from dead, whether you need to self-host or fine-tune models, and why communication and engineering fundamentals are still the biggest predictors of success.

If you're wondering what skills to invest in next—or trying to break into AI engineering—this episode is for you.

What actually defines an AI engineer?
Why system design matters more than ever
Is RAG still relevant in 2026?
The difference between AI users and AI engineers
Why engineering fundamentals still matter
Communication as a technical skill
Why experimentation is part of the job
Do you need to fine-tune or self-host models?
How Gauntlet AI prepares engineers for AI careers
Advice for learning AI without chasing every new framework

Gauntlet AI — https://gauntletai.com
LLMs in Production — https://www.manning.com/books/llms-in-production
Claude Code — https://www.anthropic.com/claude-code
Ray — https://www.ray.io/
Qwen — https://qwen.ai/blog?id=qwen3.5
UV Package Manager — https://docs.astral.sh/uv/

Domesticating AI is a bi-weekly podcast for software engineers building practical AI systems. We cover self-hosted AI, agents, infrastructure, context engineering, security, and the engineering practices that make AI reliable in production.

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Keep your AI on a leash.