AI Systems Architect

Architect scalable, secure, and intelligent AI systems from the ground up.

• 5 Hours Live Online Event (Two 2.5 hour sessions)
• 5 Hours of Self-Paced Practicum

The AI Systems Architect course is built for professionals who are ready to design intelligent systems that are not only scalable and secure but also production-ready from day one. Rather than focusing solely on tools, this program emphasizes the architectural thinking needed to build and orchestrate real-world LLM-powered systems across infrastructure, agents, APIs, and memory layers.

Moreover, this course is the first step in preparing for the W3CB AI Architect+ Certification, the industry-standard credential for enterprise AI deployment leaders. Throughout 10 structured modules and a real-world capstone, you’ll learn to optimize system cost and performance, implement observability and feedback loops, and manage compliance with governance frameworks like NIST and ISO 42001. Whether you’re deploying on Docker, Lambda, or LangGraph, this course ensures you’re architecting with foresight, and certified for it.

Prerequisites

• Completion of AI Automation Engineer (Auto+)
• Completion of AI Agent Manager (Agent+)
• Familiarity with APIs, JSON, vector stores, and prompt engineering
• (Preferred) Experience with cloud platforms or DevOps workflows

Target Audience

• AI solution architects, platform engineers, and systems integrators
• Technical product managers leading AI adoption
• ML/AI ops engineers scaling agent workflows and pipelines
• Enterprise architects planning distributed LLM deployments
• CTOs, CIOs, and senior tech leaders seeking strategic AI infrastructure capability

Learning Objectives

By completing the AI Systems Architect course, learners will be able to:

• Design modular AI architectures using agents, APIs, vector memory, and workflows
• Deploy AI systems across containerized, serverless, and hybrid infrastructures
• Implement observability, feedback loops, and agent evaluation protocols
• Manage cost, latency, prompt reuse, and system performance
• Build compliant, secure, and auditable systems aligned with enterprise AI governance