Building an AI Platform Team in a Regulated Enterprise: Roles, Skills, and Structure

You can buy tools. You can hire consultants. But eventually, a regulated enterprise deploying AI at scale needs an internal AI platform team. The question is: what should it look like?

The Three Capability Pillars

1. ML Platform Engineering

Engineers who build and maintain the ML infrastructure: training pipelines, feature stores, model serving, experiment tracking. They speak Kubernetes, Python, and GPU.

2. Data Engineering

Engineers who build and maintain the data infrastructure: ingestion pipelines, data quality frameworks, feature computation, data cataloging. In regulated environments, they also own data lineage and consent propagation.

3. AI Governance Engineering

The role that doesn't exist at most companies — but should. Engineers who translate regulatory requirements into platform capabilities: model risk management automation, compliance monitoring, audit trail infrastructure, policy-as-code. They bridge the gap between legal/compliance teams and platform engineering.

Team Size by Maturity

• Starting (1-10 models) — 3-5 people, full-stack AI platform engineer profile, external partner for governance architecture
• Scaling (10-50 models) — 8-12 people, specialized roles emerge, dedicated governance engineering
• Mature (50+ models) — 15-25 people, platform product management, SRE for ML, dedicated compliance automation

The Partner Model

Most regulated enterprises are in the "Starting" phase. They don't need 25 people — they need 3-5 strong engineers supported by an external AI infrastructure partner who provides: architecture guidance, governance framework design, implementation accelerators, and knowledge transfer. The goal is building internal capability, not permanent dependency.