The sequence mirrors how modern AI teams work: build AI applications, design intelligent agents, engineer production systems, then deploy and integrate. Modules 4 and 5 ship a hands-on project every session.
Get the map of the field, then set up the engineering habits the rest of the cohort depends on.
Where generative AI sits in the wider field, and where the jobs are.
The toolchain and code hygiene every later session assumes.
Use AI to build AI — the workflow practitioners actually work in.
Open the box. You can’t debug context windows, latency, or hallucination without knowing what the model is actually doing.
How attention replaced recurrence, and why it mattered.
From raw text to a served response: the full model lifecycle.
Control the model, ground it in your data, then prove it works — the three things that separate a demo from a product.
Designing what the model sees — instructions, tools, and state.
Grounding answers in your own knowledge, with citations.
Measuring quality on open-ended output, and catching regressions.
The core of the cohort: orchestrate workflows, design agents, engineer for production, then deploy and integrate. Each session ends with a project you keep.
Build scalable LLM applications with modern AI frameworks.
Design autonomous systems that reason, plan, and execute.
The engineering practices required to ship AI systems.
Deploy at scale and integrate external tools over MCP.
Turn the work into a portfolio, then rehearse the interviews that get you hired.
Build an end-to-end production AI application in teams.
Practice the rounds an AI engineer actually faces.
Guest practitioners on how AI gets built where they work.
Tell us about your background and what you want to build. About five minutes to complete.
Express interestQuestions first? Email aiengineerlabs@gmail.com.