Applied AI engineering path
Become a full-stack AI engineer by building real AI products.
A project-driven learning platform for developers who want to move beyond chatbot demos and build production-style AI systems with Python, FastAPI, LLMs, RAG, vector search, workflows, evaluation, and deployment.
16
Modules
271+
Lectures
Projects
Each course
8 wk
Roadmap
Project-based courses
Learn by building complete AI applications
Private workspace model
Course-specific content, learner progress, practice history, and future tracks.
Real system design story
Every course ends with a serious project and a clear interview narrative.
Why this exists
Most AI tutorials stop where real engineering begins.
You learn prompts, build a chatbot, and still cannot explain architecture, retrieval, evaluation, security, cost, or deployment in an interview. This platform is built around product proof.
Solution
Learn AI engineering the way products are actually built.
AI Engineer Guide combines structured learning with course-specific projects, practice, and career outcomes. Each course is designed for a different level of experience and a different product build.
LLM API engineering
Use model APIs with prompts, structured outputs, validation, retries, streaming, and cost tracking.
Document intelligence
Parse PDFs, understand scanned document limits, classify documents, and extract business fields.
Vector search and RAG
Build embeddings, semantic search, grounded Q&A, citations, and unknown-answer handling.
AI workflows
Design controlled agents for contract review, invoice summaries, comparisons, and checklists.
Evaluation and observability
Measure extraction accuracy, retrieval quality, citation correctness, latency, and token cost.
Security and deployment
Handle private documents with tenant isolation, RBAC, safe logging, Docker, queues, and production readiness.
Capstone
Every course comes with a real project, not just lessons.
The flagship AI Engineer Guide uses a document intelligence SaaS project. Future courses can use different builds, different skill levels, and different career outcomes while sharing the same learning platform.
Beginner-to-advanced tracks
Course-specific capstones
Practice and interview loops
Progress saved per course
AI answer review tools
Launch-ready portfolio assets
Roadmap
A complete path from full-stack developer to applied AI engineer.
Foundation
Career Strategy
Python for AI Engineering
AI / ML / LLM Foundations
Document Intelligence
Document AI Fundamentals
LLM API Engineering
Structured Extraction
Retrieval and Architecture
Embeddings and Vector Search
RAG for Business Documents
Full Stack AI SaaS Architecture
Production Readiness
Agentic Workflows
Evaluation and Observability
Security and Privacy
Deployment and LLMOps
Career Launch
Capstone Build
Interview Preparation
Resume, LinkedIn, Job Strategy
Available now
Start with the AI Engineer Guide. More tracks can plug into the same platform.
Build the AI portfolio project your resume is missing.
Learn the concepts, build the product, explain the architecture, and walk into interviews with a real story.