AI Platform Product Manager
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- Translate developer and platform needs into product requirements
- Own the LLM lifecycle, evaluation and release gates
- Make platform economics, reliability and governance tradeoffs
Benchmark: Cross-industry AI/LLM platform product management signals (current Oct 2026) Mastery ladder: 2 current-market competencies per level; 2 substantive learning bridges per competency (12 total).
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Translate developer and platform needs into product requirements
Platform product foundations and GenAI literacy
Coverage: Platform Product Management / APIs / Generative AI
Internal developer platforms and self-service
Coverage: Internal Developer Platforms / Developer Experience
Own the LLM lifecycle, evaluation and release gates
LLM lifecycle, versioning and serving workflows
Coverage: LLMOps / Model Versioning / Fine-Tuning
Automated evaluation and release gates
Coverage: AI Evals & Monitoring / CI Release Gates / Quality
Make platform economics, reliability and governance tradeoffs
Latency, cost and platform economics
Coverage: Platform Economics / Latency-Cost Tradeoffs / Reliability SLOs
Platform governance, roadmap and responsible AI
Coverage: Governance / Roadmap / Responsible AI
Role-Readiness Proof / Capstone
Create a platform PRD and 2-quarter roadmap for a shared LLM inference and evaluation service: include persona journeys, API/SDK adoption, cost per successful request, p95 latency, evaluation pass rates, release controls, a risk register, and a build-versus-buy recommendation. Present a 10-minute executive investment case.
Proof Rubric
- Demonstrate platform-product judgment: translate developer/platform needs into requirements
- show LLM lifecycle and reliability fluency
- defend latency-quality-cost tradeoffs
- and ground governance, SLOs and roadmap decisions in adoption and unit economics