AI Engineering: The Developer's Path

6 months duration

3 modules

Updated Apr 23, 2026

Development & Programming

Course Overview

Get to know what this course is all about and what you'll learn

Course Description

A six-month program that takes learners from their first line of Python to a deployed AI system — welcoming complete novices and working developers alike, with a full month on Python fundamentals before moving into LLMs, RAG, agents, evaluation, and production deployment.

What You'll Learn

AI Engineering: The Developer's Path is a six-month program that takes learners from their first line of Python all the way to a deployed AI system. Unlike most AI courses, which assume comfort with software engineering, this path begins with a full month on Python fundamentals — making it genuinely accessible to novices while still rewarding for developers who want to solidify their foundation. From there, it builds through database design, API development with FastAPI, machine learning concepts, and then the technologies that define modern AI: large language models, prompt engineering, LangChain, retrieval-augmented generation, and autonomous agents with LangGraph.

Where most AI courses stop at "it works on my machine," this one continues into the disciplines that separate prototypes from products: AI-specific user experience patterns, evaluation frameworks and testing, debugging LLM applications, cost engineering, and full MLOps including containerization, cloud deployment, observability, and compliance. Learners finish with advanced topics — fine-tuning with LoRA and QLoRA, multi-modal AI, and AI safety — before consolidating everything into a portfolio-worthy capstone project.

This program is designed for curious beginners, career changers, and working software developers who want to become the AI engineer on their team. By the end, learners will have built, evaluated, and deployed a complete end-to-end AI application, and will be fluent in the architectural decisions — build vs. buy, which model for which task, when to use RAG vs. fine-tuning, what to measure — that real AI engineering work requires every day.

Course Curriculum

3 modules • Learn at your own pace • Hands-on experience

Course Modules

Master the fundamental tool that every professional developer uses daily. Learn to track changes, collaborate with others, and manage your code like a pro from the very beginning of your development journey.

What you'll learn

Understand version control concepts and why Git is essential for modern software development
Use GitHub effectively for remote repositories, collaboration, and showcasing your work to potential employers
Master Git basics including repositories, commits, branches, and merging for effective code management.

Python serves as the foundation of modern data science, providing essential programming skills for data manipulation, analysis, and machine learning. This module develops your Python proficiency from basics through data science applications.

You'll master Python fundamentals including data types, control structures, functions, and essential libraries. Hands-on exercises with real datasets teach you to write efficient code for data processing tasks and establish the foundation for advanced data science work.

What you'll learn

Write efficient Python code using data types, control structures, and functions for data science applications
Import and utilize essential Python libraries and packages for data manipulation and analysis
Handle file input/output operations and work with different data formats (CSV, JSON, etc.)
Debug Python code effectively and implement error handling techniques
Apply object-oriented programming concepts to organize and structure data science projects
Write clean, readable code following Python best practices and coding standards
Process and manipulate datasets using core Python programming techniques

This module introduces students to fundamental database design principles and SQL (Structured Query Language). Students will learn how to design efficient relational database schemas, implement entity-relationship diagrams, normalize databases, and write SQL queries to create, retrieve, update, and delete data. The course covers both theoretical concepts and practical applications, with hands-on exercises using industry-standard database management systems. By the end of this module, students will be able to design and implement a functional database solution for real-world applications.

What you'll learn

Design normalized relational database schemas that minimize redundancy and maintain data integrity
Create entity-relationship diagrams to model real-world data relationships
Implement database tables with appropriate data types, constraints, and relationships
Write SQL queries to create, retrieve, update, and delete data from databases
Perform complex data retrieval using SQL joins, subqueries, and aggregate functions
Master advanced query techniques using Common Table Expressions (CTEs) for recursive and hierarchical data
Apply window functions for advanced analytics, rankings, and running calculations
Develop stored procedures, triggers, and user-defined functions for business logic implementation
Apply transaction management concepts to ensure data consistency and integrity