SkillHack › Career guides › AI Platform Engineer
How to become an AI Platform Engineer
Internal SDKs, model gateways, routing, and guardrail infrastructure. Build the platform every team ships AI on.
- 5phases in the roadmap
- 15topics to work through
- 17graded practice questions
- Freeno payment, ever
The AI Platform Engineer roadmap
Each topic below carries a study note, real reading, and practice questions in the app. A topic unlocks when you pass the one before it, so the order is the path, so you are never guessing what to learn next.
Phase 1AI platform engineering foundations
Platform thinking
Platform thinking asks which capabilities every team rebuilds (auth, retries, routing, logging) and turns them into a shared service with a clear contract.
Internal SDKs and abstractions
Internal SDKs should present a stable, provider-neutral contract (logical model tiers, one message type) and absorb provider quirks so callers don't couple to them.
Building for other engineers
Building for other engineers means treating internal teams as customers.
Phase 2AI platform engineering model gateway
Gateways and routing
A model gateway centralizes routing so callers ask for a logical tier and task, not a provider id.
Rate limits and quotas
Shared upstream limits create noisy-neighbor risk: one tenant's burst can 429 everyone.
Multi-provider abstraction
Supporting multiple providers means a canonical internal request/response model plus a thin adapter per provider (an anti-corruption layer).
Phase 3Guardrails and infra
Guardrail infrastructure
Centralize guardrails in the gateway as a configurable pre/post pipeline: PII redaction, prompt-injection checks, and policy filters.
Caching layers
Caching cuts cost and latency, but one mechanism won't fit every pattern.
Observability platform
Observability answers where the time or error went across caller to gateway to guardrails to provider.
Phase 4AI platform engineering developer experience
SDKs and docs
Structure docs by user need: a quickstart to reach a first working call, task-oriented how-tos, and a complete reference, each a different shape (see Diataxis).
Self-serve onboarding
Self-serve onboarding removes the platform team from the critical path: automated scoped-credential issuance, sane default quotas with a request-increase path, and a guided getting-started flow.
Templates and golden paths
A golden path (paved road) is an opinionated, batteries-included default that makes the well-supported way the easy way: a starter template and CLI that scaffold a service pre-wired to gateway,…
Phase 5AI platform engineering in production
Reliability and scaling
Provider outages and latency spikes are inevitable; the platform's job is to contain blast radius.
Cost governance
Cost governance starts with attribution: tag every request's token cost to a team/feature, then expose per-tenant dashboards, budgets, and alerts with soft/hard limits.
Platform security
A shared gateway holding the company's provider keys is a high-value target and a multi-tenant boundary.
Reading for this path
The primary sources behind the topics above, all free to read.
- Martin Fowler: What I talk about when I talk about platforms
- Team Topologies: key concepts
- Anthropic API: client SDKs
- Azure Architecture: Anti-corruption Layer pattern
- Martin Fowler: Products over projects
- Azure Architecture: Gateway Routing pattern
- Anthropic: models overview
- Anthropic API: rate limits
- Azure Architecture: Throttling pattern
- Anthropic: tool use overview
- OWASP Top 10 for LLM Applications
- Azure Architecture: Gatekeeper pattern
- Anthropic: prompt caching
- Azure Architecture: Cache-Aside pattern
Coming from another job?
Most people on this path arrived from somewhere else: engineering, analysis, testing, product, support, compliance, design. Onboarding asks what you do today and builds a short starter run out of the gaps, so you begin from what you already know rather than from chapter one. See how it works.
Other AI career paths
AI Policy Analyst
Regulation, standards, risk, and societal impact. Translate AI policy into what teams must actually build.
AI Product Manager
Product scoping, model capability awareness, eval-driven decisions, and AI UX trade-offs. Decide what to build and prove it works.
AI Program Manager
Scoping, sequencing, risk, and cross-team coordination. Drive AI initiatives from research to launch.
AI Quality Engineer
Behavioral test suites, regression and red-team checks, and release gates. Test AI products that never give the same answer twice.
AI Red Team Specialist
Jailbreaks, adversarial testing, misuse probing, and vulnerability reporting. Attack AI systems before real adversaries do.
AI Reliability Engineer
Uptime, fallbacks, guardrails, and incident response for AI in production. Keep AI features fast, safe, and available.
Start the AI Platform Engineer path for free
The full study notes, the reading, and the practice questions behind every topic above are in the app. Answer one tonight and you have started.
No payment, no credit card, no CV. Sign in with Google.