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This guide focuses on AI platform team for production systems, with practical trade-offs for reliability, security, and cost.
AI platform work dies when “everyone owns it” or when a single embedded researcher owns eval for the company. Topology matters: platform team for gateway/eval rails; embedded partners for product UX.
⚡ TL;DR: Platform owns gateway, budgets, tracing, prompt registry, shared eval harness. Product squads own feature prompts, golden sets, and UX. Hire for security + systems + ML eval literacy — not demo-prompting only.
AI platform team: production guidance
| Capability | Platform | Squad |
|---|---|---|
| AI Gateway | ✅ | consume |
| Cost dims / budgets | ✅ | set limits |
| Feature prompt | templates | ✅ |
| Golden eval set | harness | ✅ cases |
| Tool IAM | ✅ patterns | request |
text
Platform (2–6 eng) ── rails ──► many squads
Embedded AI eng (optional) sit in high-stakes squads
Hiring signals
- Shipped production systems with SLOs
- Can read IAM and threat models
- Comfortable with eval statistics
- Skeptical of vanity accept rates (Day 42/56 vibe)
Failure modes
Platform team becomes a ticket sink for every prompt tweak. Publish self-serve prompt registry docs. Embedded hires without production ownership create demo culture — pair them with a systems mentor.Closing checklist
- [ ] RACI published
- [ ] Eval ownership explicit per feature
- [ ] Platform staffing matched to gateway load
- [ ] Career path for AI platform eng
- [ ] Quarterly topology review
Series navigation
Day 95: Incident Response When the Agent Goes Wrong · Day 97: Build vs Buy the Agent Runtime Last updated September 11, 2026 Related: Day 80: Project: Platform AI Gateway; Day 76: p99 of Agents: Queueing, Not Just Model Latency; Day 77: Caching RAG Answers Safely.Watch: 100 Days of AI on YouTube
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