Define the AI problem before defining the candidate
Employers should distinguish between applied AI, machine learning, MLOps, model research, data science, computer vision, NLP and generative-AI implementation.
Separate research from production engineering
A strong machine-learning researcher may not have the same experience as an engineer who deploys models into production systems. Search criteria should reflect the actual work environment.
Reach passive AI talent
Many qualified AI professionals are heavily recruited and rarely rely on traditional job applications. Direct outreach is therefore especially important.
Evaluate real project experience
Employers should look beyond broad AI terminology and evaluate the candidate's work with models, datasets, deployment environments, infrastructure and measurable business outcomes.
Compete on more than salary
AI candidates may also evaluate technical challenge, access to data, computing resources, leadership quality, product influence and long-term career opportunity.
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