The AI-Native Engineer

Two categories  /  Eight modules  /  71 videos

The AI-Native
Engineer

Half of this course makes you faster at the job you already have: an AI-augmented editor, architecture you diagram before you code, debugging from the stack trace up, refactors and pipelines you no longer write by hand. The other half changes the job — you stop calling models and start shipping them: local LLMs, retrieval over your own data, live computer vision, and the infrastructure to serve all of it in production.

Start with Module 1 See the course map
Each part
Lecture + hands-on video, guide, assignment, quiz
Each module
Four parts, then a capstone build
You need
An editor, a terminal, and an AI assistant

The course in two halves

Category 1: AI-Assisted Software Development

Category 1 of 2

AI-Assisted Software Development

Focus Using AI to drastically accelerate the traditional coding, debugging, and architectural workflow

The AI-Native Engineer: Module 1, The AI-Augmented Environment Module 1 of 8

The AI-Augmented Environment

Retrofit the workspace and learn how to program, rather than chat with, an LLM IDE integration (Cursor/Copilot), the R-I-C-E prompting framework, managing context windows to prevent hallucinations, and strict data privacy. Open moduleClose module
The AI-Native Engineer: Module 2, Architectural Planning & System Design Module 2 of 8

Architectural Planning & System Design

Use AI to conceptualize and diagram high-level systems before writing code Generating product requirement documents, AI-assisted database schema design, and prompting for Mermaid.js architecture diagrams. Open moduleClose module
The AI-Native Engineer: Module 3, Accelerated Code Generation & Debugging Module 3 of 8

Accelerated Code Generation & Debugging

Write boilerplate, prototype rapidly, and resolve logic errors instantly Zero-to-draft generation, API endpoint creation, deciphering complex stack traces with AI, and isolating edge cases without breaking existing logic. Open moduleClose module
The AI-Native Engineer: Module 4, Intelligent Refactoring & CI/CD Module 4 of 8

Intelligent Refactoring & CI/CD

Modernize legacy code, ensure quality, and automate deployments Prompting for clean architecture refactors, automating unit/integration test generation, and using AI to write GitHub Actions YAML for CI/CD pipelines. Open moduleClose module
Category 2: Applied AI Engineering

Category 2 of 2

Applied AI Engineering

Focus Transitioning from using AI as a tool to building proprietary, AI-native applications

The AI-Native Engineer: Module 5, The Deep Learning & Hugging Face Ecosystem Module 5 of 8

The Deep Learning & Hugging Face Ecosystem

Understand the underlying mechanics of modern AI models and how to source them Deep learning architectural fundamentals for full-stack developers, navigating the Hugging Face Hub, and understanding model weights and quantization. Open moduleClose module
The AI-Native Engineer: Module 6, Local LLMs & RAG Architectures Module 6 of 8

Local LLMs & RAG Architectures

Escape vendor lock-in and build secure pipelines for proprietary data Setting up Ollama for local inference, configuring vector databases (Pinecone/ChromaDB), and wiring up a Retrieval-Augmented Generation (RAG) pipeline. Open moduleClose module
The AI-Native Engineer: Module 7, Applied Computer Vision with OpenCV & YOLO Module 7 of 8

Applied Computer Vision with OpenCV & YOLO

Integrate real-time environmental awareness into traditional software Setting up an OpenCV environment, feeding live video streams into YOLOv8 models, and parsing bounding box data back into a user interface. Open moduleClose module
The AI-Native Engineer: Module 8, Deploying AI-Native Applications Module 8 of 8

Deploying AI-Native Applications

Architect the infrastructure required to host and serve machine learning models Cloud GPU hosting strategies (like Replicate or RunPod), containerizing local models with Docker, and optimizing inference speeds for production environments. Open moduleClose module

Carry this out of the course

Three habits of an AI-native engineer

  1. 01 Program the model. Give it role, input, constraints, and the exact shape of the output you want.
  2. 02 Own the context. What the model can see is what it can be right about — and what it cannot leak.
  3. 03 Ship the model, not the API key. If the data is yours, the inference should be too.

Tools change every quarter. The engineer who understands why a model answered the way it did, and can run one on hardware they control, does not have to relearn the job each time.