Subvolts take AI jobs demand mid-level skills; academia needs to adapt to real-world needs. Dec 10, 2025 - Developers
- Learn about the practical skills employers seek beyond theoretical knowledge for AI engineering roles.
- Business
- Understand the talent pipeline challenges for AI roles and how academia might adapt to industry needs.
This episode features a practical discussion on a significant challenge for the AI workforce and education system.
The AI engineer skills gap
Audio streams from Daniel Whitenack and Chris Benson. Your place is remembered on this device.
From Subvolts
Summary and analysis by Subvolts editorial, AI-assisted.
In this Practical AI episode, hosts Daniel Whitenack and Chris Benson discuss the AI engineer skills gap with guest Ramin Mohammadi. They explore how companies expect candidates to have mid-level experience for AI engineer and data science roles, not entry-level. The conversation also covers potential academic adjustments to focus on real-world skills over theoretical knowledge.
Many AI jobs expect you to already be an experienced professional, even for entry-level roles. This episode talks about this problem and how schools could teach more practical skills instead of just theory.
This episode addresses a critical bottleneck for aspiring AI professionals: the expectation of mid-level experience for entry-level jobs. It's for job seekers and educators alike, highlighting a disconnect between academic training and industry needs. As frontier labs like Google Gemini and OpenAI rapidly advance AI capabilities, the demand for skilled engineers who can implement these innovations grows. This discussion offers insights into bridging that gap, making it relevant to anyone following the practical application of AI.
- HostDaniel Whitenack
- HostChris Benson
- GuestRamin Mohammadi
- Episode Title: The AI engineer skills gap
- Show: Practical AI
- Guest: Ramin Mohammadi
- Length: 46 minutes
- Key Issue: AI jobs expect mid-level engineers, not entry-level.
Chapters from the publisher ↗ · Transcript ↗ Key moments and transcript search come with a later piece.
Chris and Daniel talk with returning guest, Ramin Mohammadi, about how those seeking to get into AI Engineer/ Data Science jobs are expected to come in a mid level engineers (not entry level). They explore this growing gap along with what should (or could) be done in academia to focus on real world skills vs. theoretical knowledge.
Featuring:
- Ramin Mohammadi – LinkedIn
- Chris Benson – Website, LinkedIn, Bluesky, GitHub, X
- Daniel Whitenack – Website, GitHub, X
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- AI Engineer
- Daniel Whitenack, Chris Benson, Ramin Mohammadi
- A professional who builds and maintains artificial intelligence systems.
- Data Science
- Daniel Whitenack, Chris Benson, Ramin Mohammadi
- The field of extracting knowledge and insights from data using scientific methods and algorithms.
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Questions people ask
- What is the main problem discussed regarding AI jobs?
- The episode highlights that companies expect candidates to possess mid-level engineering skills for AI engineer and data science positions, rather than entry-level qualifications.
- What is suggested for academia?
- It is suggested that academia should shift its focus to teaching real-world skills relevant to industry needs, rather than solely concentrating on theoretical knowledge.
Page generated Oct 19, 2026. Summaries are Subvolts' own; the audio, artwork and notes belong to Daniel Whitenack and Chris Benson.