AI Systems Architect
AI Systems Architect
Architect scalable, secure, and intelligent AI systems from the ground up.
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.
Courses in this Certificate Program
- 10 HOURS TOTAL
- Live: 5 Hours
- Self-Study: 5 Hours
- Tuition: $795
| Components of this Course | Hours | Delivery Method |
|---|---|---|
| AI Systems Architect | 10 Hours | Live Online & OnDemand |
| Live Instructor AMA - bi-monthly | 1 Hour+ | Live Online (optional) |
Upcoming LIVE Instruction Dates
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
Course Modules Breakdown
1: AI Systems Thinking
- Stateless/stateful models, legacy integration, agent decoupling
- Assignment: Draw a 3-agent, 2-service AI system map
2: Infrastructure & Deployment Models
- Docker, Vercel, Lambda, CI/CD
- Workshop: Deploy containerized agent workflow
3: Observability & Performance
- LangSmith, Prometheus, logs vs. metrics vs. traces
- Assignment: Build observability dashboard
4: Orchestration Strategies
- LangGraph, Prefect, retries and compensation logic
- Workshop: Orchestrate a branching pipeline
5: Multi-Agent Ecosystem Design
- Lead/fallback agents, message routing, negotiation protocols
- Assignment: Design and simulate 3-agent workflow
6: Cost & Latency Optimization
- Caching, token efficiency, prompt chaining
- Workshop: Reduce token cost by 25%
7: Security & Access Governance
- RBAC, PII masking, audit trails, secrets management
- Assignment: Write an AI security compliance policy
8: A/B Testing & Iteration
- Prompt versioning, feedback scoring, rollbacks
- Workshop: Run A/B test and compare outcomes
9: Enterprise Governance & Compliance
- NIST, ISO 42001, failover plans, transparency
- Assignment: Write full compliance strategy
10: Capstone Project – Full AI System Deployment
- Requirements: 2 agents, 1 orchestrator, 2+ integrations
- Deliverables: video walkthrough, logs, system map, config files
AI Course Information
- Review Live Dates Below
- Online & OnDemand
- Tuition: $795
- Ask about Tuition Assistance
- 10 Hours
Additional Information
- Module Quizzes and Knowledge Checks
- Guest Lectures & Networking
- LIVE Online Instructor AMAs
- Certification Exam Prep
This course prepares you to sit for the Web3 Certification Board (W3CB) AI Architect+ Certification.
Job Titles You May Qualify For
- AI Systems Architect
- LLM Infrastructure Engineer
- AI Platform Lead
- Intelligent Automation Architect
- Enterprise AI Strategist
- Head of AI Engineering
- Applied AI Governance Lead
Income Expectations
- Mid-Level AI Architects: $130,000–$160,000/year
- Senior/Lead AI Engineers & Architects: $160,000–$190,000/year
- Enterprise AI Strategists / Heads of AI: $190,000–$225,000+/year
- Salaries vary based on industry, system complexity, and region.
Reference Sources: ZipRecruiter, Glassdoor, Payscale, and Lightcast.io