Practical AI The AI engineer skills gap
180 snips
Dec 10, 2025 
Ramin Mohammadi, an adjunct professor and lead AI engineer, dives into the widening skills gap for aspiring AI engineers and data scientists. He reveals that industry now favors mid-level expertise, challenging academic institutions to adapt. Topics include the rise of full-stack expectations, the impact of generative AI on junior roles, and the urgent need for hands-on MLOps training. Ramin also discusses mentorship's critical role in preserving cognitive skills in a tech-driven landscape, and offers insights on how high school programs can better prepare future talent.
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Hiring Values Production Skills
- Industry hiring now values building and owning scalable AI systems over textbook knowledge.
- Ramin Mohammadi says deployment, monitoring, and MLOps are the new currency for employers.
GenAI Raised The Entry Bar
- Generative AI automated many repeatable junior tasks and raised hiring expectations.
- Ramin Mohammadi notes companies now hire for proven capabilities rather than potential.
Prioritize Practical Skills In Courses
- Teach practical, hands-on skills alongside theoretical foundations in curricula.
- Ramin Mohammadi argues that skills to ship real work matter more than knowledge alone.