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AI Engineer Guide: build Arkion DocIntel end to end.
A complete applied AI engineering path for full-stack developers. Learn by building a business document intelligence SaaS with upload, extraction, embeddings, RAG, workflows, evaluation, security, and deployment.
16
Modules
271+
Lectures
10
Build phases
8 wk
Plan
What you build
Outcomes
You finish with interview stories, not just notes.
Explain AI systems clearly in interviews
Build Python and FastAPI AI services
Implement semantic search and RAG
Extract structured fields from documents
Evaluate hallucination, retrieval, citations, cost, and latency
Present a portfolio-ready AI SaaS project
Curriculum
The complete path from career strategy to launch readiness.
Every module maps to a product capability, an interview story, or a career asset.
Module 0
Career Strategy & Product Direction
Clear target role, product direction, capstone scope, and interview positioning.
Module 1
Python for AI Engineering
Build an AI-ready FastAPI document upload service with tests and clean structure.
Module 2
AI, ML, Generative AI & LLM Foundations
Explain core AI, ML, embeddings, transformers, inference, hallucination, and RAG tradeoffs.
Module 3
Document AI Fundamentals
Build a document text extraction and metadata pipeline.
Module 4
LLM API Engineering
Build reusable AI service calls for summaries, classification, and extraction.
Module 5
Structured Document Extraction
Extract and store invoice, contract, policy, and proposal fields.
Module 6
Embeddings, Vector Search & Semantic Search
Search documents by meaning and return relevant chunks.
Module 7
RAG for Business Documents
Ask questions over one or many documents with cited, grounded answers.
Module 8
Full Stack AI SaaS Architecture
Create the complete architecture blueprint, API contracts, schema plan, job model, and UI route map.
Module 9
Agentic Document Workflows
Implement classifier, contract review, invoice approval, comparison, and checklist workflows safely.
Module 10
AI Evaluation, Testing & Observability
Build golden datasets, eval scripts, usage tracking, prompt regression checks, and a quality dashboard plan.
Module 11
AI Security, Privacy & Compliance
Create the security model for document access, vector privacy, secure uploads, audit logs, and PII-aware processing.
Module 12
Deployment, Scaling & LLMOps
Create Docker, environment, health check, deployment, monitoring, README, and demo video plans.
Module 13
Capstone Build: Arkion DocIntel
Turn the entire course into a portfolio-grade product with upload, extraction, search, RAG, workflows, evals, security, and deployment.
Module 14
AI Engineer Interview Preparation
Prepare your pitch, project deep dive, AI concepts, system design answers, behavioral stories, and final checklist.
Module 15
Resume, LinkedIn & Job Strategy
Create the AI-focused resume, LinkedIn profile, GitHub proof, case study, outreach scripts, and application tracker.
Ready to study and build?
Open the dashboard, start from Module 0, and work through the capstone path with the product beside you.