AI Engineering: Skill Stack, Agents, LLMOps, and How to Ship AI Products
Shipping real AI products is now one of the most in-demand engineering skills, but most teams still get stuck turning prototypes into something that actually works.
In this podcast, AI engineer and bestselling author Paul Iusztin breaks down the full AI engineering skill stack:
The principles behind production-grade systems
How to build reliable agentic workflows
The LLMOps practices that keep models stable
The patterns he’s learned from shipping more than 20 AI applications
We’ll also go beyond the code. Paul will share how he structures his work, teaching, writing, and professional growth, and how he uses AI tools to stay focused, productive, and consistent.
Join us live if you want a straightforward look at the technical and personal side of modern AI engineering.
About the Speaker:
Paul Iusztin is an AI engineer committed to helping developers create fully functional, production-grade AI products. He is the author of the bestselling "LLM Engineer’s Handbook," leads the Agentic AI Engineering course, and is a founding AI engineer at a startup based in San Francisco. He also Decoding AI Magazine, where he assists engineers in moving beyond the proof-of-concept stage to build more effective AI systems.
With over ten years of experience, Paul teaches comprehensive AI engineering, covering everything from data gathering to deployment, monitoring, and evaluation. He emphasizes robust software practices, infrastructure, and principles that are reliable in a world increasingly influenced by AI coding tools.
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