Certified AI Engineer (CAIE™): Agents, RAG & LLM Systems
Become an AI Engineer who ships real, production-grade AI code. A hands-on coding program: build one enterprise AI system week by week in Python, with RAG, tools, LangChain, LangGraph multi-agent systems, guardrails and AWS deployment.
Skills employers are hiring for now
- Companies need engineers who can turn LLMs into reliable, secure software, not just demos.
- You write the code yourself: every week adds real files to one enterprise project.
- You learn when to use plain Python, LangChain, LangGraph or AWS-native services, and why.
- You finish with a deployed, tested AI system and a portfolio repository employers can read.
Built for real enterprise work
Hands-on coding in every lesson: 120 activities, each adding real, tested Python to one repository you build on your laptop, including 30 senior deep dives and production incident studies.
Every activity explains which file you add or change, which library you use and why.
Real-world examples from Canadian banking, telecom, healthcare and government.
Video lessons with Toro, where the exact MapleDesk code is typed out and explained line by line, with “how to think” and “how not to think” in every lesson.
10 modules, fundamentals to production
Tap a module to see what you learn in it.
Python for AI Engineering
Set up MapleDesk, the enterprise support assistant you build all course: typed, tested, configurable Python with async I/O.
- What AI engineers actually build
- Project layout, uv and pyproject.toml
- Settings and secrets done right
- A pinned model registry that cannot drift: senior deep dive
- Type hints that catch bugs before users do
- Pydantic models for messages and answers
- Errors as part of the design
- Discriminated unions for model output: senior deep dive
- Async Python for slow AI calls
- pytest fixtures and fake models
- Build the MapleDesk skeleton
- Record-and-replay tests for LLM calls: senior deep dive
What you'll become, and what it can do for your pay
About 52% higher at the middle of the range
- The core job skills employers list most: Python, models, APIs, data pipelines and evaluation.
- Real projects that show you can take a model from notebook to working product.
- A clear path from developer or analyst into one of the fastest-growing job titles in tech.
- A foundation for every senior AI role: architect, MLOps, security or research.
"AI Engineer" is one of the fastest-growing job titles in Canada and the US, and most teams cannot find enough people who can ship models, not only train them.
Estimates from 2025–26 job postings (base salary). Typical ranges, not a guarantee: pay depends on your experience, city, employer and the job market.
Where graduates go
MapleDesk: an enterprise AI assistant
A deployed, tested multi-agent system with RAG over company documents, tool integrations, guardrails, evaluations and CI/CD on AWS, built by you in one repository.
Portfolio-grade project
Earned by shipping code, not just by attending.
Your CAIE™ certificate shows you built, tested and deployed a real AI system: the MapleDesk project, built by you in one repository.
- Complete all 10 weeks and the MapleDesk capstone
- Pass the final exam: 20 questions in 60 minutes, 14 correct to pass
- Receive a certificate with your name, date and a unique certificate ID
- Employers scan the QR code or visit our verify page to confirm it is real
Proof you can ship AI, not just talk about it
Backed by a real, deployed project
Behind the certificate is MapleDesk: a multi-agent system with RAG, tools, guardrails, evals and CI/CD that you built in one repository. You can walk any interviewer through it.
Named after the job employers hire for
“AI Engineer” is one of the fastest-growing job titles in Canada and the US. Your certificate says exactly that, and lists the skills behind it.
Skills hiring managers search for
Agents and orchestration, RAG and vector databases, LLM apps and prompt engineering, cloud deployment, security and governance: all printed on the certificate.
Checked in seconds
Every certificate has a unique ID and a QR code. A recruiter scans it and sees your name, program and date on our verify page.
Estimates from 2025–26 job postings (base salary). Typical ranges, not a guarantee: pay depends on your experience, city, employer and the job market.
Admission
Questions
Is this program suitable for beginners?+
Some programming helps, but the curriculum takes committed professionals from the foundations to enterprise deployment, step by step.
Is it fully online?+
Yes. It is delivered 100% online with live, practitioner-led sessions, so you can join from anywhere.
Do I get career support?+
Yes. You build a portfolio-grade capstone, practise technical interviews and get guidance on the roles you are targeting.
What jobs can I get after graduating?+
Graduates move into roles such as AI Engineer, LLM Engineer, Applied AI Developer, AI Agent Developer, Machine Learning Engineer (GenAI).
Ready to become an AI Engineer?
Join the next cohort and build production-ready AI with practitioners.
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