Produced by PodRescale · 12 Dec 2025Hosted by Daniel Whitenack

The AI engineer skills gap

The content pack PodRescale made from this episode of Practical AI: 17 finished assets, all public.

Is the AI job market still the gold mine it was a decade ago?

45:33Original feed

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In the pack:Show notes8 quotesNewsletterTranscript

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01Show notes54 words
02Chapters7 · timestamped
03Quote cards8 cards
04Newsletter3 subject lines
05Episode titles12 title options
06Summaries2 versions
07Transcript7,867 words
08Assets2 versions

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It’s not about what you know from the textbook anymore—it’s about what you can build, deploy, and maintain in a real, scalable AI system.

Chris BensonPractical AI · 3:25

TranscriptPractical AI · 45:33Play this moment

3:25Chris Benson

… It's not about what do you know about from the textbook anymore, it's about what can you build, can you deploy and maintain a real scalable AI system? It's kind of like that's the new currency of hiring.

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Show notes · as generated

This episode of the Practical AI Podcast explores the growing skills gap facing AI engineers, data scientists, and machine learning professionals. Hosts Daniel Whitenack and Chris Benson are joined by guest Ramin Mohammadi to discuss the changing demands in both industry and academia, and how generative AI and MLOps have transformed the required skillset.

Ramin reflects on the early 2010s surge in data science demand, the rapid expansion of university programs, and how the promise of easy employability has faded. Entry-level AI jobs now require more than textbook knowledge.

Discussion of how MLOps has redefined success in AI roles, with skills like containerization, CI/CD pipelines, deployment, and monitoring now essential. Data scientists must now 'own the pipeline' from modeling to production.

AI engineering roles now demand a blend of deep technical skills, practical deployment expertise, and the ability to adapt as automation reshapes entry-level tasks.

It’s not about what you know from the textbook anymore—it’s about what you can build, deploy, and maintain in a real, scalable AI system.

Pull quote · Chris Benson · from 3:25

Generative AI wasn’t just a cool new tool—it was an automation event that immediately attacked the entry point in the field.

Pull quote · Chris Benson · from 6:35

Newsletter example

Why AI Engineers Need More Than a Degree in 2024

What does it really take to thrive as an AI engineer today? In this episode, I sit down with Ramin Mohammadi—adjunct professor and principal AI engineer—to unpack the growing gap between what universities teach and what industry actually demands.

As someone who’s lived in both worlds, I’ve seen firsthand how the expectations for data scientists and machine learning engineers have shifted. The entry-level roles many of us started with? They’re vanishing, replaced by higher bars and more complex responsibilities. Ramin and I discuss why being ‘full stack’—from modeling to deployment—is more than a buzzword, and why the mid-level is now the new starting point.

We also get honest about the limitations of academic programs. I share my own story from walking Purdue’s campus: in 2017, a Hall of Data Science made sense. Today, you need a Hall of AI. Even with name changes, the gap persists.

If you’re wondering how to future-proof your career—and what skills will actually set you apart—this episode is for you.

Listen now

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