The Core AI Team
A capable AI team typically includes: ML engineers who build and deploy models, data engineers who manage data pipelines, data scientists who analyze data and prototype solutions, and AI product managers who translate business needs into AI projects.
Smaller teams may combine roles. A startup might have one ML engineer who also handles data engineering, and a PM who also handles evaluation.
Beyond the Technical Roles
Effective AI teams also need: domain experts who understand the business problem deeply, ethicists or responsible AI specialists who assess risks and bias, and MLOps engineers who handle deployment, monitoring, and infrastructure.
Do not underestimate the importance of domain expertise. A mediocre ML model built by someone who deeply understands the problem often outperforms a sophisticated model built by someone who does not.
Hiring Strategies
AI talent is expensive and competitive. Consider: training existing employees with domain expertise in AI tools, hiring junior talent and investing in development, partnering with AI consultancies for specialized projects, and using AI platforms that reduce the need for deep ML expertise.
Team Structure
Three common models: Centralized (one AI team serves the whole company), Embedded (AI specialists sit within product teams), or Hub and spoke (central platform team with embedded specialists). The right model depends on company size and AI maturity.