Most companies hire for ML skills when they need DevOps expertise. MLOps is 70% production engineering, 30% machine learning. Hire accordingly or your models gather dust in notebooks.
Great prompt engineers combine systematic thinking with creative problem-solving. Here is how to find them, test them, help them grow, and avoid the usual hiring mistakes mid-size companies make.
Stop hiring for today AI skills. The field changes too fast. Hire for learning ability instead - it is the only skill that will not go stale in six months.
Most AI product manager job descriptions copy traditional PM templates and miss what actually matters - the ability to translate between technical teams and business stakeholders without losing meaning in either direction. Learn how to write job descriptions that attract interpreters who can bridge data science and business worlds.
Most companies hire the wrong AI research scientist because they copy job descriptions from DeepMind and OpenAI. They need applied researchers but attract pure researchers instead. Here is what mid-size companies actually need and how to hire the right one.
Production experience beats research publications when hiring ML engineers. Write job descriptions that attract engineers who ship models, not just train them.
Process expertise beats deep technical knowledge when hiring AI Operations Managers. Most companies get this backwards, prioritizing ML engineer skills over operational wisdom. Only 1 in 10 AI prototypes reach production - that is an operations problem, not a technology problem.
Best consultants are translators and educators who bridge technical complexity with business reality. Most companies hire PhD-level experts who cannot explain anything. Here is how to find consultants who truly deliver real value and transform your business through clear communication.