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.
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 AI trainers with technical credentials, then wonder why training fails. Teaching experience trumps technical depth. Adult learning principles, curriculum design, and facilitation skills drive adoption better than machine learning expertise. Here is what to look for in an AI trainer job description
Most computer vision engineer job descriptions miss the most critical requirement - embedded systems knowledge for edge deployment where the real work happens
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.