AI Factory Manager / AI Industrialization Lead
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- Define the AI factory operating model and production intake
- Industrialize deployment, reliability and AI factory economics
- Govern a scalable AI production system and measurable value engine
Benchmark: NVIDIA AI Factory / DSX operating model + enterprise AI-factory/MLOps leadership signals (current Sep 2026) Current bar: industrialize AI from intake to production; unify data, compute, MLOps, evals, governance, capacity and economics; optimize time-to-value, utilization, reliability and cost per intelligence unit.
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Define the AI factory operating model and production intake
AI factory operating model + use-case portfolio
Coverage: AI Strategy / Use-Case Discovery / Portfolio Intake
Data, model + MLOps production foundation
Coverage: MLOps / Data Platforms / Model Lifecycle
Industrialize deployment, reliability and AI factory economics
Production pipelines, serving + observability
Coverage: Deployment / Monitoring / SLOs / Incident Learning
Compute capacity, utilization + token economics
Coverage: AI Infrastructure / Capacity / FinOps / Unit Economics
Govern a scalable AI production system and measurable value engine
Evals, security, governance + release gates
Coverage: AI Evals / Governance / Security / Model Risk
Operating cadence, teams + enterprise adoption
Coverage: AI Operating Model / Leadership / Adoption / Value Realization
Role-Readiness Proof / Capstone
Operate a simulated enterprise AI factory: create intake and stage gates for 8-10 use cases; define data/model/platform architecture; establish CI/CD/CT, model registry, eval and approval gates; forecast accelerator demand; track utilization, tokens/sec, tokens/watt and cost/token; implement observability and incident response; publish an executive factory scorecard and quarterly capacity/value plan.
Proof Rubric
- Prove repeatable flow from idea to production
- measurable cycle time and deployment quality
- reproducible MLOps
- credible capacity/utilization economics
- evaluation and rollback evidence
- governance proportional to risk
- clear ownership and operating cadence
- and quantified business value rather than model demos