Q: What projects should be in an AI Engineer portfolio? How many?

Three to four strong, deployed end-to-end AI projects beat twenty toys. Every single time.

A strong portfolio arc covers the stack in the order employers test it: a RAG application (for example, a chatbot grounded in a real knowledge base), a multi-agent system (an orchestrator directing specialist agents), an agent connected to real tools via MCP, and a production deployment on AWS with monitoring, security, and CI/CD. One project in your target industry is worth extra: it shows judgment, not just skills.

What makes a project count is verifiability. Deployed with a live URL a recruiter can click. A README that explains the architecture and the decisions. A commit history that shows the work is yours.

Want to see what outstanding looks like? Here's a real example, built by one of our students: Viet's portfolio — scroll down to "Generative AI and LLM Systems." Four AI projects, every single one deployed with a live demo a recruiter can click. That's the standard to aim for.