The Minimum Viable AI Team
Three people can take a model from data to production. A Data Engineer builds pipelines and ensures data quality. An ML Engineer owns model development and production deployment. An MLOps Engineer handles monitoring, versioning, and CI/CD automation. These three can move fast and cover the full lifecycle. If you only have budget for three, this is what you need.
Scaling to 10
Add Data Scientists for experimentation and model innovation. Hire a Security Engineer focused on governance and adversarial testing. Bring on a Product Manager who translates business problems into technical requirements. Add specialist ML Engineers — one for NLP, one for computer vision, or one for embeddings, depending on your product. This team now has specialization and can tackle bigger, more complex problems.
Common Mistakes
Hiring Data Scientists first without Data Engineers. Data Scientists get frustrated waiting for data pipelines. Skipping MLOps because "we're still experimenting." Models stuck in notebooks never reach production. Neglecting security until it's too late. AI systems have new attack surfaces, and security should be baked in from the start. No product management. Engineers can't prioritize what to build without understanding business impact.
Reporting Structure
Should AI report to CTO, Chief Product Officer, or its own function? It depends on AI maturity. Early stage: report to CTO (focus on hiring and execution). Mid-stage: report to CPO if AI drives product differentiation, or to CTO if it's infrastructure-focused. Mature: separate function (VP AI or Head of AI) reporting to CEO or COO. Get this wrong, and you'll see constant priority misalignment.
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