Start with the operating use case
Define who will use the capability, which decision or workflow changes, and how value will be measured. This clarifies whether the first hire should be a builder, product leader, domain specialist or governance lead.
Design the capability chain
Applied AI needs more than a model. Consider data foundations, experimentation, evaluation, product integration, monitoring, security, change and accountable decision-making.
Some capability may already exist in engineering, product, legal or risk teams. Hiring should close the real gaps rather than recreate an isolated AI function.
Distinguish the core roles
AI and Machine Learning Engineers focus on building and operating systems. Data Scientists investigate and model. AI Product leaders connect user problems to delivery. MLOps specialists strengthen deployment and monitoring. Responsible AI and model-risk specialists establish controls and oversight.
Hire for production evidence
Explore what a candidate personally delivered, how it was evaluated, what happened after launch and how limitations were communicated. Production judgement is more informative than familiarity with the newest terminology.