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The Enterprise AI Reference Architecture

Most enterprise AI deployments have the same seven layers, and a design round is largely a check that you can name them and say what fails in each. Having the reference in your head turns an ambiguous prompt into a checklist you walk, which is why strong candidates never look lost in a system-design interview.

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RELATED CONCEPTS

The Walking Skeleton (Thin Slice First)VPC and Air-Gapped Deployment Observability for AI Systems RBAC, ABAC and Identity at Runtime for Agents Retrieval-Augmented Generation (RAG)

LESSONS THAT TEACH THIS

Foundations of Forward Deployed EngineeringSpecify the ownership, trust, network and meaning at each boundary28 min→The FDE EngagementBuild the diagnostics you will need when you cannot log in28 min→

PRACTICE THIS IN REAL QUESTIONS

RAG & Agent System DesignWalk me through a RAG pipeline end-to-end, and tell me where it usually breaks→ML Infrastructure & GPUsExplain how the CUDA execution model maps to hardware, grids, blocks, warps, SMs.→ML System Design (Product)Design the ranking model for a personalized feed (Instagram-style).→ML Infrastructure & GPUsWalk me through the GPU memory hierarchy, registers, shared memory, L2, HBM. What lives where and why?→ML System Design (Product)Design a music recommendation system (Spotify-style).→ML Infrastructure & GPUsWhat is warp divergence and why does it hurt performance?→