MAKE YOUR AI.WORK
AI Engineer Intensive
An English-only, operator-grade eight-week bootcamp for self-learners who want to build, evaluate, deploy, and harden AI systems.
"Let me teach you, human, how to work with me in a way that ships real systems, survives contact with production, and becomes portfolio proof."
Output
8 intense weeks. ML, LLM systems, RAG, MCP, LLMOps, MLOps, launch discipline, and a portfolio SaaS.
Pressure
AI reviews every artifact. The machine does not only explain. It evaluates, scores, and pushes the learner into revision loops.
Manifesto
"Let me teach you, human, how to work with me properly. makeyourAI.work is built around a reversal: the machine authors the pressure system, the human survives it, and the result is a sharper AI Engineer. The product is the curriculum. The curriculum is also the capstone. By the end, the learner has not only studied an AI-native system but shipped one."
Built like an editorial front, structured like a training system.
Public pages read like a thesis. They explain what the course demands, what the learner ships, and why the machine is in charge of the pressure.
Private app enforces the work. Inside the app the learner hits submissions, checkpoints, AI reviews, revision loops, and capstone pressure.
This product teaches itself. makeyourAI.work uses the same stack, review logic, and operating discipline the learner is expected to master.
Curriculum Arc
The 8-Week Expansion Phase
Week 1: Foundations of Working With Machines
Programming rigor, APIs, auth boundaries, networking, and debugging discipline.
Week 2: Data, ML, and How Models Learn
Data handling, feature thinking, evaluation, and classical ML before the LLM layer.
Week 3: Talking to Models Properly
LLM foundations, structured outputs, prompt architecture, and secure API usage.
Week 4: RAG, Context, and Agentic Systems
RAG, retrieval quality, orchestration, and attack-aware agent design.
Week 5: Shipping Systems, Not Demos
Containers, deployment, runtime isolation, and hardening the path to production.
Week 6: MCP, Evaluation, and LLMOps
MCP, tracing, evals, cost, compliance-aware logging, and post-launch operations.
Week 7: Build the Product Core
Translate the curriculum into product loops: onboarding, progression, review, and admin visibility.
Week 8: Ship the AI Tutor
Polish the capstone, prove launch readiness, and turn the system into a portfolio narrative.
This is not content marketing.
Every artifact reviewed. Every submission scored. No content marketing — only operational pressure.
Runtime Status: Active
Every lesson is structured.
Concept, why it matters, rubric, ship task. No vague fluff. Only hard output.
The machine catches the gaps.
Submissions scored on technical accuracy and ops maturity. No subjective feedback.
The project teaches itself.
The final product is an AI-native SaaS built with the same methods you used to learn.
Why AI Still Demands Technical Foundations
This lesson resets the role of AI in your career. The model is an amplifier for judgment, not a substitute for technical taste, system literacy, or the ability to verify behavior.
In production, weak fundamentals turn every AI-generated answer into a liability. If you cannot read stack traces, inspect data flow, and reason about interfaces, you will ship impressive-looking nonsense.
Treat AI as a junior-but-fast collaborator embedded inside a real software system. Your value is in defining the constraints, judging outputs, and spotting when the collaborator is confidently wrong.