AI Architect: Enterprise AI System Design

Enterprise AI architecture for senior engineers: design production AI systems — RAG, agents, model selection, governance, evaluation, and cost at scale.

Every AI certification out there teaches you how to build: how to train models, wire up pipelines, or call an LLM API. This course teaches you how to design.

Who this course is for

Senior engineers, solutions architects, enterprise architects, and technical leads who've been handed the AI initiative. If that's you, you already know the real questions have nothing to do with which API to call.

The questions this course answers

• Should we fine-tune or use RAG? Module 3 separates knowledge problems (RAG) from behaviour problems (fine-tuning), and gives you a five-dimension model scorecard to defend either choice to stakeholders.

• How do we expose an AI capability to a fifteen-year-old enterprise system without rewriting it? Module 4 designs the centralized AI gateway, and the sidecar pattern that integrates with a legacy monolith without touching it.

• What happens when the model is confidently wrong, and who's accountable? Module 4 builds a three-layer validation stack with matched fallback strategies; Module 5 architects the human oversight and audit trail behind it.

• How do we satisfy the EU AI Act, GDPR, and HIPAA without stalling the build? Module 5 maps each regulation — plus the NIST AI RMF — to the concrete architectural requirement it imposes on a system you're designing today.

• How do we prove the system actually works? Module 6 builds a three-layer evaluation stack: infrastructure telemetry, LLM evaluation with golden datasets and RAGAS, and business outcome metrics tied back by correlation ID.

• Can we afford to run this at real scale? Module 7 models cost at launch, 10x, and projected peak, then designs caching, model routing, and cost circuit breakers before they're needed.

What you'll design

Seven modules and a running capstone covering the complete AI Architect skill set: AI feasibility assessment and scoping a vague request into a buildable system; RAG and agent architectures with their production failure modes handled, not ignored; model strategy across proprietary API, open-weight, and fine-tuned deployment; enterprise integration through a proper gateway layer; governance, AI security, and the OWASP Top 10 for Agentic Applications; a three-layer evaluation stack; and cost, scaling, and LLMOps at production scale. Tooling walkthroughs cover LiteLLM, Langfuse, RAGAS, vector databases (Pinecone, pgvector, Qdrant, Weaviate, Chroma), HNSW and IVF index architecture, and the MCP and A2A agent protocols.

Every framework is applied to realistic case studies spanning insurance, fintech, healthcare, and e-commerce.

What this course is not

It is not a model-training tutorial, not a machine learning course, and not a coding bootcamp. No AI/ML background, no Python, and no data science experience is required — the course starts from first principles. It does assume real engineering fluency: APIs, distributed systems, and at least one cloud platform. If you want to train models, this is the wrong course. If you're going to be accountable for an AI system in production, it's the right one.