Super Data Science: ML & AI Podcast with Jon Krohn 899: Landing $200k+ AI Roles: Real Cases from the SuperDataScience Community, with Kirill Eremenko
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Jun 24, 2025 Kirill Eremenko, Founder and CEO of SuperDataScience.com and a leading data science instructor on Udemy, shares captivating subscriber success stories in AI and data science. He discusses the essential skills needed, revealing why Python and SQL remain vital. Networking and in-person events are highlighted as keys to landing top roles. Eremenko also explores the balance of foundational knowledge and adapting to industry changes, emphasizing the importance of communication and continuous learning in a rapidly evolving field.
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Alex's AI Engineer Interview Success
- Alex landed an AI engineer job by preparing on fundamentals and participating in a collaborative real-world project.
- His ability to brainstorm LLM applications impressed interviewers, combining theory with practical experience.
Fundamentals Still Crucial
- Employers still test AI engineer candidates on foundational machine learning concepts despite focus on LLMs.
- This hedge ensures candidates possess essential skills beyond using APIs and abstraction layers.
AI Engineering Focus Shift
- AI engineering roles currently focus more on applying existing LLM tools than on deep model mechanics.
- Understanding prompting, fine-tuning, and model application is more practical for many jobs than mastering underlying training algorithms.
