Vibe-coded AI prototypes ship in days. They do not survive production. By the time the founder learns about token cost runaway, p99 latency, fan-out concurrency, hallucination mitigation in agentic loops, vector-store TCO, or the difference between a fine-tune and a system prompt — the rebuild has already cost more than this programme.
This module is the AI Architecture course we wish every Tier-III flagship student took before launching. It exists for two audiences: senior engineers and intrapreneurs who want the system-design depth without a full programme, and MasterAI flagship students who want to add an architecture credential on top.
Benchmarked against Carnegie Mellon's Advanced Enterprise Architecture (twelve weeks) and Agentic AI Programme (seven weeks), we deliver a deeper, longer module at a comparable price.
Senior software engineers transitioning into AI lead roles. Engineering managers responsible for AI-product technical strategy. Founders launching technical products who need the architecture vocabulary to brief and review their engineering teams. Tier-II and Tier-III MasterAI students adding depth on the architecture axis.
Either the completion of MasterAI Tier I or II, or three-plus years of professional software engineering experience. A short technical interview verifies readiness. We do not admit candidates who have never shipped a production service before.
Reference architectures for inference systems. The boring layers — auth, routing, observability, cost control — that decide whether a product survives traffic.
Frontier vs open-weight vs self-hosted. Token-cost economics. Multi-model routing strategies. The architecture choice that compounds.
Vector stores in production. Chunking strategy, hybrid search, BM25 + embedding fusion, re-ranking. Cost vs accuracy curves at scale.
Agent loops, plan-act-observe, tool calling, fan-out concurrency, error boundaries. Why most agentic systems fail in production and how to design them so they don't.
Evaluation harnesses, regression testing for LLM outputs, prompt versioning, observability tooling, telemetry pipelines.
Latency budgets, p99 management, batching, KV-cache reuse, vLLM and TGI deployment, GPU clustering, edge serving.
Prompt injection defence, PII handling, audit logging, regulated-industry deployment (healthcare, finance), key management, region-locked inference.
Each student produces a complete architecture document for a production AI system of their choosing — and ships the system. Reviewed by industry guest panel.
All fees exclusive of GST. Payment terms identical to standard MasterAI programmes — ten percent on offer acceptance, balance at module start.
Online form with current engineering background.
Forty-five minutes verifying production engineering depth.
Short design exercise reviewed by faculty.
Decision within seven days of architecture sketch submission.