AI & LLM Engineering with Python – Data Science Academy

About Course

Become an AI Engineer in 6 Months

The most comprehensive AI & LLM engineering track in Arabic — built for serious professionals who want to build real AI systems, not just watch tutorials.

From your first Python script to deploying production-grade multi-agent systems, this track takes you through 17 modules covering everything that matters in 2026: LLMs, RAG, AI agents, LangGraph, MCP, fine-tuning, AI quality engineering, FastAPI, and deployment.

You won’t just learn theory. You’ll build 3 portfolio-grade capstone projects, master the modern AI stack, and finish with a complete consulting playbook to monetize your skills.

17 Modules • 214 Lessons • 109 Hours • 3 Real Capstones

If you can write Python, you can become the AI Engineer companies are paying $80K–$180K for. This track gets you there.

Build production-grade AI agents from scratch using Python and the latest LLM APIs
Master Claude, GPT-4, Gemini, and open-source models — and pick the right one every time
Design advanced RAG systems with hybrid search, reranking, and GraphRAG
Build multi-agent systems with LangGraph including state management and human-in-the-loop
Create custom MCP (Model Context Protocol) servers that connect AI to any tool or database
Engineer AI quality systems with LangSmith — evaluation pipelines, drift detection, CI/CD for AI
Build full AI backend APIs with FastAPI — streaming, authentication, rate limiting, caching
Deploy AI applications to production with Docker, AWS, and CI/CD pipelines
Fine-tune Llama and Mistral models with LoRA and QLoRA on consumer hardware
Build multimodal AI apps with vision, voice, and document intelligence
Master prompt engineering with evaluation-driven techniques used by professionals
Ship 3 portfolio-grade capstone projects that land jobs and clients
Price, scope, and deliver AI consulting projects profitably
Land AI Engineering roles paying $80K–$180K or build a $1,500/day consulting practice

Course Content

Module 1: AI Foundations & The LLM Revolution (4 hours, 10 lessons)

1.1 The AI Landscape in 2026 — Where We Are Now
1.2 From Rules to Machine Learning to LLMs (No Math Required)
1.3 How Transformers Actually Work — The Intuitive Guide
1.4 Tokens, Tokenization, and Why It Matters for Cost
1.5 Embeddings — The Hidden Language of AI
1.6 Context Windows, Memory, and Attention
1.7 Open vs Closed Models — Claude, GPT, Gemini, Llama, Mistral
1.8 Reasoning Models — How Thinking Modes Change Everything
1.9 When to Use AI vs Traditional ML vs Simple Code
1.10 The AI Engineer Mindset — How to Think in Probabilities

Module 2: Python for AI Engineering (5 hours, 10 lessons)

2.1 Modern Python Setup — uv, pyenv, and Project Structure
2.2 Type Hints and Why They Matter for AI Code
2.3 Pydantic — The Secret Weapon for LLM Outputs
2.4 Async/Await — Making API Calls 10x Faster
2.5 Working with JSON, Streaming, and Generators
2.6 Environment Variables, Secrets, and .env Patterns
2.7 HTTP Clients — httpx vs requests vs aiohttp
2.8 Error Handling, Retries, and Exponential Backoff
2.9 Logging and Observability Basics
2.10 Building a Reusable AI Utilities Package

Module 3: Working with LLM APIs (6 hours, 12 lessons)

3.1 Anthropic Claude API — Deep Dive (Sonnet, Opus, Haiku)
3.2 OpenAI API — GPT-4 Family and Reasoning Models
3.3 Google Gemini API — Long Context Champions
3.4 Open Source via Groq, Together, and Fireworks
3.5 Streaming Responses — Real-Time Token Generation
3.6 Structured Outputs — JSON Mode and Schema Enforcement
3.7 Prompt Caching — Cut Costs by 90% on Repeat Calls
3.8 Token Counting and Cost Estimation
3.9 Rate Limits, Retries, and Production Resilience
3.10 Building a Universal LLM Wrapper (LiteLLM Pattern)
3.11 Model Routing — Right Model for Right Task
3.12 Comparative Benchmarking on Your Own Tasks

Module 4: Prompt Engineering Mastery (6 hours, 13 lessons)

4.1 The Anatomy of a Production Prompt
4.2 System Prompts vs User Prompts — The Right Architecture
4.3 Few-Shot Learning — When and How
4.4 Chain-of-Thought and Step-by-Step Reasoning
4.5 XML Structuring — Claude’s Superpower
4.6 Role Prompting and Persona Engineering
4.7 Output Formatting — Forcing JSON, Tables, Markdown
4.8 Negative Prompting and Constraints
4.9 Prompt Chaining and Decomposition
4.10 Self-Critique and Self-Refinement Loops
4.11 Building a Prompt Evaluation Framework
4.12 A/B Testing Prompts at Scale
4.13 Common Failure Modes and How to Fix Them

Module 5: Tool Use & Function Calling (5 hours, 11 lessons)

5.1 What is Function Calling? The Mental Model
5.2 Anatomy of a Tool Schema — JSON Schema Mastery
5.3 Your First Tool — Calculator and Weather
5.4 The Agentic Loop — Letting AI Iterate
5.5 Parallel Tool Calling for Speed
5.6 Tool Choice — Forcing, Auto, and None
5.7 Database Tools — Letting AI Query Safely
5.8 API Tools — Connecting to External Services
5.9 File System and Code Execution Tools
5.10 Error Handling When Tools Fail
5.11 Tool Use Best Practices and Anti-Patterns

Module 6: RAG — Retrieval-Augmented Generation (8 hours, 18 lessons)

6.1 Why RAG? The Problem with Pure LLMs
6.2 Embeddings Deep Dive — How Semantic Search Works
6.3 Embedding Models — OpenAI, Cohere, Voyage, Open Source
6.4 Vector Databases Compared — Chroma, Pinecone, Qdrant, pgvector
6.5 Chunking Strategies — Fixed, Semantic, and Hierarchical
6.6 Document Loaders — PDF, Word, HTML, Markdown
6.7 Building Your First RAG Pipeline (End-to-End)
6.8 BM25 and Keyword Search Fundamentals
6.9 Hybrid Search with Reciprocal Rank Fusion (RRF)
6.10 Reranking — The Quality Multiplier
6.11 Query Transformation, Rewriting, and HyDE
6.12 Multi-Hop Retrieval Patterns
6.13 Metadata Filtering and Permission-Aware Retrieval
6.14 Multi-Vector and ColBERT Approaches
6.15 Agentic RAG — Letting the LLM Plan Retrieval
6.16 GraphRAG — Knowledge Graphs for Complex Queries
6.17 RAG Evaluation — Faithfulness, Context Precision, Recall
6.18 Production RAG Architecture and Cost Optimization

Module 7: Building AI Agents from Scratch (7 hours, 12 lessons)

7.1 What is an Agent? Cutting Through the Hype
7.2 The ReAct Pattern — Reasoning + Acting
7.3 Building a ReAct Agent from Scratch
7.4 Plan-and-Execute Agents
7.5 Reflection and Self-Improvement Loops
7.6 Agent Memory — Short Term and Long Term
7.7 Multi-Step Workflows and Decomposition
7.8 Multi-Agent Systems — When and How
7.9 Human-in-the-Loop Patterns
7.10 Cost and Latency Control for Agents
7.11 Debugging Agent Failures
7.12 Agent Evaluation and Benchmarks

Module 8: LangChain Essentials (4 hours, 8 lessons)

Module 9: LangGraph Production Mastery (10 hours, 18 lessons)

9.1 LangGraph vs LangChain vs Raw API — The Decision Tree
9.2 Graph Fundamentals — Nodes, Edges, and State
9.3 Your First LangGraph Agent — Building from Scratch
9.4 State Schema Design — TypedDict and Pydantic Patterns
9.5 Conditional Edges and Branching Logic
9.6 Cycles, Loops, and Iterative Refinement
9.7 Subgraphs and Modular Agent Composition
9.8 Checkpointing — Persisting Agent State
9.9 Memory Architectures — Thread, User, and Long-Term
9.10 Human-in-the-Loop with Interrupts
9.11 Time-Travel Debugging and State Replay
9.12 Streaming Tokens, Steps, and State Updates
9.13 Multi-Agent Orchestration Patterns
9.14 Supervisor, Hierarchical, and Network Architectures
9.15 Production Deployment of LangGraph Agents
9.16 Performance Optimization — Parallelism and Caching
9.17 LangGraph Cloud and Self-Hosted Options
9.18 Real-World Case Studies — Customer Support, Research, Coding Agents

Module 10: MCP — Model Context Protocol (5 hours, 12 lessons)

10.1 What is MCP and Why It Changes Everything
10.2 MCP Architecture — Servers, Clients, and Hosts
10.3 Setting Up Your First MCP Server in Python
10.4 Exposing Tools via MCP
10.5 Exposing Resources and Prompts
10.6 Connecting Claude Desktop to Your Server
10.7 Building a Database MCP Server
10.8 Building a File System MCP Server
10.9 Building an API Wrapper MCP Server
10.10 Authentication and Security in MCP
10.11 Deploying MCP Servers in Production
10.12 The MCP Ecosystem and What’s Next

Module 11: Fine-Tuning & Model Customization (6 hours, 12 lessons)

11.1 Fine-Tuning vs Prompting vs RAG — The Decision Framework
11.2 How Fine-Tuning Actually Works (Conceptually)
11.3 Dataset Preparation — The 80% That Matters
11.4 Synthetic Data Generation with LLMs
11.5 Hugging Face Ecosystem Tour
11.6 LoRA — Low-Rank Adaptation Explained
11.7 QLoRA — Fine-Tuning on Consumer Hardware
11.8 Fine-Tuning Llama and Mistral with Unsloth
11.9 Fine-Tuning OpenAI and Anthropic Models
11.10 Evaluation — Did Fine-Tuning Actually Help?
11.11 Deploying Fine-Tuned Models
11.12 Cost Analysis — When Fine-Tuning Pays Off

Module 12: Multimodal AI — Vision, Audio, and Beyond (6 hours, 12 lessons)

12.1 The Multimodal Landscape in 2026
12.2 Vision with Claude and GPT-4o — Practical Patterns
12.3 Document Understanding — PDFs, Forms, Tables
12.4 OCR vs Vision LLMs — When to Use Each
12.5 Building a Document Intelligence Pipeline
12.6 Image Generation — DALL-E, Stable Diffusion, Flux
12.7 Speech-to-Text with Whisper
12.8 Text-to-Speech and Voice Cloning
12.9 Real-Time Voice Agents
12.10 Video Understanding and Generation
12.11 Multimodal RAG — Searching Across Media
12.11 Multimodal RAG — Searching Across Media

Module 13: AI Quality & Observability Engineering (8 hours, 16 lessons)

13.1 Why AI Quality Is Different From Software QA
13.2 The AI Observability Stack — LangSmith, Langfuse, Arize Compared
13.3 LangSmith Deep Dive — Setup, Tracing, and Datasets
13.4 Distributed Tracing for Multi-Step Agents
13.5 Building Eval Datasets That Actually Catch Bugs
13.6 LLM-as-Judge — Patterns, Pitfalls, and Calibration
13.7 Offline Evaluation Pipelines (CI/CD for Prompts)
13.8 Online Evaluation — Sampling Production Traffic
13.9 Faithfulness, Groundedness, and RAG-Specific Metrics
13.10 Agent Trajectory Evaluation — Did the Agent Do It Right?
13.11 Human-in-the-Loop Annotation Workflows
13.12 Drift Detection and Quality Alerts
13.13 A/B Testing Prompts and Models in Production
13.14 Building Feedback Loops — User → Eval Set → Improvement
13.15 Cost and Latency Monitoring at Scale
13.16 The AI Quality Engineer Role — Career Path and Salaries

Module 14: Building AI APIs with FastAPI (8 hours, 16 lessons)

14.1 Why FastAPI Is the Standard for AI Backends
14.2 FastAPI Fundamentals — Routes, Dependencies, Pydantic
14.3 Async APIs for High-Concurrency LLM Calls
14.4 Streaming Responses with Server-Sent Events (SSE)
14.5 WebSockets for Real-Time AI Chat Interfaces
14.6 Background Tasks for Long-Running AI Jobs
14.7 Authentication, API Keys, and User Management
14.8 Rate Limiting and Per-User Quotas
14.9 Database Integration — PostgreSQL and Redis
14.10 Caching AI Responses — Semantic and Exact Match
14.11 Error Handling and Retry Logic for AI Endpoints
14.12 Security — Prompt Injection, Input Validation, PII
14.13 File Uploads for Multimodal AI APIs
14.14 Testing FastAPI AI Applications
14.15 OpenAPI Documentation and Client SDK Generation
14.16 Production-Ready FastAPI Project Structure

Module 15: Deployment & DevOps for AI (5 hours, 10 lessons)

15.1 Docker for AI Apps — Multi-Stage Builds and Best Practices
15.2 Docker Compose for Local Development
15.3 Deploying to Railway, Render, and Fly.io
15.4 AWS Deployment — ECS, Lambda, and Bedrock
15.5 CI/CD Pipelines for AI Apps with GitHub Actions
15.6 Environment Management — Dev, Staging, Production
15.7 Secrets Management — Vault and AWS Secrets Manager
15.8 Load Testing AI Applications
15.9 Monitoring Costs and Setting Budget Alerts
15.10 Incident Response for AI Systems

Module 16: Capstone Projects — Three Real Builds (12 hours, 12 lessons)

16.1 Capstone 1 — RAG-Powered Customer Support Agent (Setup)
16.2 Capstone 1 — Building the Knowledge Base
16.3 Capstone 1 — Agent Logic, Quality Gates, and Escalation
16.4 Capstone 1 — FastAPI Backend, Frontend, and Deployment
16.5 Capstone 2 — Multi-Agent Research System (Setup)
16.6 Capstone 2 — Agent Architecture with LangGraph
16.7 Capstone 2 — MCP Integration for Tools
16.8 Capstone 2 — Quality Evaluation and Report Generation
16.9 Capstone 3 — Document Intelligence Platform (Setup)
16.10 Capstone 3 — Vision Pipeline for Documents
16.11 Capstone 3 — RAG Layer and Query Interface
16.12 Capstone 3 — Full Deployment, Quality System, and Handoff

Module 17: AI Career & Consulting Track (4 hours, 12 lessons)

17.1 The AI Job Market in 2026 — Roles and Salaries
17.2 Building Your AI Portfolio That Gets Interviews
17.3 Acing AI Engineering Interviews
17.4 Going Freelance — Platforms and Positioning
17.5 The AI Consultant Playbook
17.6 Pricing AI Projects — Hourly, Fixed, Value-Based
17.7 Client Discovery and Scoping AI Projects
17.8 Writing AI Project Proposals That Win
17.9 Managing AI Project Risk and Expectations
17.10 Building Authority on LinkedIn and YouTube
17.11 Productizing Your AI Services
17.12 Your 90-Day Action Plan

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About Course

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Course Content

Module 1: AI Foundations & The LLM Revolution (4 hours, 10 lessons)

1.1 The AI Landscape in 2026 — Where We Are Now

1.2 From Rules to Machine Learning to LLMs (No Math Required)

1.3 How Transformers Actually Work — The Intuitive Guide

1.4 Tokens, Tokenization, and Why It Matters for Cost

1.5 Embeddings — The Hidden Language of AI

1.6 Context Windows, Memory, and Attention

1.7 Open vs Closed Models — Claude, GPT, Gemini, Llama, Mistral

1.8 Reasoning Models — How Thinking Modes Change Everything

1.9 When to Use AI vs Traditional ML vs Simple Code

1.10 The AI Engineer Mindset — How to Think in Probabilities

Module 2: Python for AI Engineering (5 hours, 10 lessons)

2.1 Modern Python Setup — uv, pyenv, and Project Structure

2.2 Type Hints and Why They Matter for AI Code

2.3 Pydantic — The Secret Weapon for LLM Outputs

2.4 Async/Await — Making API Calls 10x Faster

2.5 Working with JSON, Streaming, and Generators

2.6 Environment Variables, Secrets, and .env Patterns

2.7 HTTP Clients — httpx vs requests vs aiohttp

2.8 Error Handling, Retries, and Exponential Backoff

2.9 Logging and Observability Basics

2.10 Building a Reusable AI Utilities Package

Module 3: Working with LLM APIs (6 hours, 12 lessons)

3.1 Anthropic Claude API — Deep Dive (Sonnet, Opus, Haiku)

3.2 OpenAI API — GPT-4 Family and Reasoning Models

3.3 Google Gemini API — Long Context Champions

3.4 Open Source via Groq, Together, and Fireworks

3.5 Streaming Responses — Real-Time Token Generation

3.6 Structured Outputs — JSON Mode and Schema Enforcement

3.7 Prompt Caching — Cut Costs by 90% on Repeat Calls

3.8 Token Counting and Cost Estimation

3.9 Rate Limits, Retries, and Production Resilience

3.10 Building a Universal LLM Wrapper (LiteLLM Pattern)

3.11 Model Routing — Right Model for Right Task

3.12 Comparative Benchmarking on Your Own Tasks

Module 4: Prompt Engineering Mastery (6 hours, 13 lessons)

4.1 The Anatomy of a Production Prompt

4.2 System Prompts vs User Prompts — The Right Architecture

4.3 Few-Shot Learning — When and How

4.4 Chain-of-Thought and Step-by-Step Reasoning

4.5 XML Structuring — Claude’s Superpower

4.6 Role Prompting and Persona Engineering

4.7 Output Formatting — Forcing JSON, Tables, Markdown

4.8 Negative Prompting and Constraints

4.9 Prompt Chaining and Decomposition

4.10 Self-Critique and Self-Refinement Loops

4.11 Building a Prompt Evaluation Framework

4.12 A/B Testing Prompts at Scale

4.13 Common Failure Modes and How to Fix Them

Module 5: Tool Use & Function Calling (5 hours, 11 lessons)

5.1 What is Function Calling? The Mental Model

5.2 Anatomy of a Tool Schema — JSON Schema Mastery

5.3 Your First Tool — Calculator and Weather

5.4 The Agentic Loop — Letting AI Iterate

5.5 Parallel Tool Calling for Speed

5.6 Tool Choice — Forcing, Auto, and None

5.7 Database Tools — Letting AI Query Safely

5.8 API Tools — Connecting to External Services

5.9 File System and Code Execution Tools

5.10 Error Handling When Tools Fail

5.11 Tool Use Best Practices and Anti-Patterns

Module 6: RAG — Retrieval-Augmented Generation (8 hours, 18 lessons)

6.1 Why RAG? The Problem with Pure LLMs

6.2 Embeddings Deep Dive — How Semantic Search Works

6.3 Embedding Models — OpenAI, Cohere, Voyage, Open Source

6.4 Vector Databases Compared — Chroma, Pinecone, Qdrant, pgvector

6.5 Chunking Strategies — Fixed, Semantic, and Hierarchical

6.6 Document Loaders — PDF, Word, HTML, Markdown

6.7 Building Your First RAG Pipeline (End-to-End)

6.8 BM25 and Keyword Search Fundamentals

6.9 Hybrid Search with Reciprocal Rank Fusion (RRF)

6.10 Reranking — The Quality Multiplier

6.11 Query Transformation, Rewriting, and HyDE

6.12 Multi-Hop Retrieval Patterns

6.13 Metadata Filtering and Permission-Aware Retrieval

6.14 Multi-Vector and ColBERT Approaches

6.15 Agentic RAG — Letting the LLM Plan Retrieval