The Ultimate AI Engineer Roadmap 2025: Step-by-Step Guide

Artificial Intelligence (AI) has emerged as one of the most transformative technologies of our time. It's not just about robots or self-driving cars - AI is reshaping industries, creating new opportunities, and driving change. As businesses continue to adopt AI, the demand for skilled AI engineers is at an all-time high. But where do you begin your AI journey? What steps should you take to become an AI Engineer in 2025?

In this comprehensive guide, we’ll break down everything you need to know to become an AI Engineer. Whether starting with no prior experience or looking to upskill, this roadmap will provide you with the tools, resources, and guidance needed to succeed in the AI field.

Table of Contents

• Why Pursue an AI Engineer Career?
• AI Engineer Roadmap 2025 Overview
• Foundation Phase (Weeks 0-6)
• Core Skills Development (Weeks 7-18)
• Advanced Topics (Weeks 19-32)
• Specialization and Career Preparation
• Conclusion: The Future of AI Engineering

1. Why Pursue an AI Engineer Career?

Before diving into the roadmap, let's address a crucial question: Are there enough jobs for AI engineers?

The answer is an emphatic yes. A quick search on popular job portals reveals thousands of AI engineer openings. Moreover, roles like Machine Learning (ML) engineers and AI engineers are seeing a significant rise, as businesses worldwide continue to adopt AI technologies. The World Economic Forum reports that AI engineers are among the fastest-growing job roles globally, with a promising future for the next 5-10 years.

2. AI Engineer Roadmap 2025 – Step-by-Step Guide to Becoming an AI Engineer

This roadmap is your step-by-step guide to becoming an AI Engineer in 2025. It's designed for absolute beginners and those looking to upskill, with a clear, structured plan that spans 8 months, although having some background will certainly speed up your progress.

3. Foundation Phase (Weeks 0-6)

Week 0: Do Proper Research and Protect Yourself from Scams

Unfortunately, the ed-tech industry, especially in the data field, is rife with systematic scams. Aspirants are often misled with false promises like a 100% job guarantee or lured into "Masterclasses" that are mere sales pitches for overpriced, low-quality courses. To avoid falling into such traps, it is crucial to thoroughly research the market and mentors before starting your AI & ML journey.

Research Resources:

Here are a few posts that can help you with your research. However, these are not sufficient, and you should do additional research beyond these sources:

• Resource 1
• Resource 2
• Resource 3
• Resource 4

Week 1: Computer Science Fundamentals

Topics Covered:

• Data representation: Bits and Bytes, Storing text and numbers, Binary number system
• Basics of computer networks, IP addresses, Internet routing protocol
• UDP, TCP, HTTP, and The World Wide Web
• Programming basics: Variables, strings, numbers, conditions, loops
• Algorithm basics

Learning Resources:
Khan Academy Course: Start Learning (Only follow the first 4 sections: Digital Information, The Internet, Programming, and Algorithms. Others are optional.)
EXTREMELY IMPORTANT: Use ChatGPT as your personal tutor in case you have doubts, and you need clarity on anything.
Skip this section if you are already a software engineer, computer science student or know the above fundamentals due to whatever reason.

Week 2: Beginner's Python

Topics Covered:

• Variables, Numbers, Strings
• Lists, Dictionaries, Sets, Tuples
• If conditions, Loops (for, while)
• Functions, Lambda Functions
• Modules (pip install), File Handling
• Exception handling, Classes, and Objects

Learning Resources:
Track A (Free)
Python Tutorials (Codebasics) - First 16 Videos
Corey Schafer's Python Tutorials
Codebasics Python Hindi Tutorials
Use ChatGPT for additional help.

Track B (Affordable Fees)
Data Science and AI Bootcamp

LinkedIn - Core Skill Development

Create a professional-looking LinkedIn profile with:
- A clear profile picture and banner image
- Relevant tags such as "Open to Work."

Hands-on Practice:
Track A: Finish all these exercises: https://bit.ly/3k1mof5
Track B: Finish exercises and quizzes for relevant topics
Create a professional-looking LinkedIn profile.

Weeks 3 & 4: Data Structures and Algorithms in Python

Topics Covered:

• Data Structures: Arrays, Linked Lists, Hash Tables, Stacks, Queues, Trees, Graphs
• Algorithms: Binary Search, Bubble Sort, Quick Sort, Merge Sort, Recursion
• Big O Notation

Learning Resources:
DSA YouTube Playlist

Motivation:
ML Engineer after 12th

Hands-on Practice:
Finish all these exercises in this same playlist: https://bit.ly/3uiW2Lf

Week 5: Advanced Python

Topics Covered:

• Inheritance, Generators, Iterators
• List Comprehensions, Decorators
• Multithreading, Multiprocessing

Learning Resources:
Python Tutorials (Codebasics) - Videos 17-27

Hands-on Practice:
Finish all these exercises in this same playlist: https://bit.ly/3X6CCC7

Core/Soft Skills Development:

LinkedIn Networking

• Follow AI influencers:
• Yann LeCun
• Daliana Liu
• Nitin Aggarwal
• Steve Nouri
• Dhaval Patel
• Increase engagement by commenting meaningfully on AI-related posts.
• Build connections and enhance your online presence.

Business Fundamentals - Soft Skill

• Learn business concepts from ThinkSchool and YouTube case studies.
• Example: How Amul Beat the Competition

Discord Community Engagement

• Ask questions and learn from the community. How to Ask Questions the Right Way
• Join the Codebasics Discord Server

Assignment

• Write meaningful comments on at least 10 AI-related LinkedIn posts.
• Note down your key learnings from 3 case studies on ThinkSchool and share them with your friend.

  Motivation

  • How Kaggle Helped This Person Become an ML Engineer

  Week 6: Version Control (Git & GitHub)

  Topics Covered:

  • Introduction to Version Control, Git, and GitHub
  • Basic Git commands: Add, Commit, Push
  • Branches, Reverting Changes, HEAD, Diff, and Merge
  • Pull Requests

  Learning Resources:
  Git & GitHub YouTube Playlist (Codebasics)
  Git & GitHub YouTube Playlist (Corey)

  Core/Soft Skills:
  Avoid Death by PowerPoint

  4. Core Skills Development (Weeks 7-18)

  Weeks 7-8: Mastering Relational Databases and SQL

  Understanding databases is crucial for data science. Start with relational databases and SQL to manage structured data efficiently.

  Topics Covered:

  • Basics of relational databases
  • Writing basic SQL queries: SELECT, WHERE, LIKE, DISTINCT, BETWEEN, GROUP BY, ORDER BY
  • Advanced SQL queries: CTE, Subqueries, Window Functions
  • SQL Joins: Left, Right, Inner, Full
  • Database creation, indexes, and stored procedures

  Learning Resources:
  Track A:
  Khan Academy SQL Course
  W3Schools SQL Tutorial
  SQLBolt Interactive Learning
  SQL Video Tutorial
  
  Track B:
  AI Bootcamp

  Hands-on Practice:

  • Participate in SQL resume project challenge on https://codebasics.io/
  • Link: https://codebasics.io/challenge/codebasics-resume-projectchallenge/7
  • These challenges help you improve technical skills, soft skills and business understanding.
  • Make a LinkedIn post with a submission of your resume project challenge.
  • Sample post: https://bit.ly/48Bg5mB

  Week 9: Exploring NoSQL Databases

  Modern applications demand flexible and scalable data storage. Learn NoSQL databases to handle unstructured data efficiently.

  Topics Covered:

  • Fundamentals of NoSQL: Scalability, unstructured data, real-time analytics
  • Data modeling in NoSQL
  • BASE vs. ACID, Partitioning & Sharding, Replication, CAP Theorem

  Learning Resources:
  Introduction to NoSQL
  MongoDB Tutorial

  Motivation:
  Mechanical to Deep Learning Engineer
  

  Week 10: Data Manipulation with Numpy, Pandas & Data Visualization

  Data preprocessing and visualization are essential for any data-driven role.

  Topics Covered:

  • Numpy for numerical computing (Playlist)
  • Pandas, Matplotlib, and Seaborn
  • Go through chapter 5 in this course (entire chapter is free): Math & Statistics for Data Science

  Weeks 11-13: Math & Statistics for AI

  Solid math and statistical foundations are key to AI & machine learning.

  Topics Covered:

  • Descriptive vs. inferential statistics, linear algebra, calculus
  • Data distributions, probability, hypothesis testing
  • Correlation, covariance, and the central limit theorem

  Learning Resources:
  Track A(Free)
  Learn the above topics from this excellent Khan academy course on statistics and probability.
  Course link: https://www.khanacademy.org/math/statisticsprobability
  While doing khan academy course, when you have doubts, use statquest YouTube channel: https://www.youtube.com/@statquest
  Use this free YouTube playlist: https://bit.ly/3QrSXis
  Another great youtube channel: https://www.youtube.com/@3blue1brown
  
  Track B (Affordable Fees)
  Learn the key concepts of Math and Statistics that lay the foundations for a strong data science career: https://codebasics.io/courses/math-and-statistics-for-datascience

  Hands-on Practice:
  Complete exercises in this playlist
  Finish all exercises in Khan academy course.
  Track B: Finish exercises and quizzes for relevant topics.

  Week 14: Exploratory Data Analysis (EDA)

  Learning Resource:
  Online retail analysis tutorial
  Practice EDA using at least 3 datasets.

  Hands-on Practice:
  Perform EDA (Exploratory data analysis on at least 2 additional datasets on Kaggle)

  Weeks 15-18: Machine Learning Foundations

  Learn core ML concepts and implement real-world projects.

  Topics Covered:
  Data preprocessing:
  - Handling NA values, outlier treatment, data normalization
  - One hot encoding, label encoding
  - Feature engineering
  - Train test split
  - Cross validation

  Topics Covered:

  • Model Building
  • Types of ML: Supervised, Unsupervised
  • Supervised: Regression vs Classification
  • Linear models
  • Linear regression, logistic regression
  • Gradient descent
  • Nonlinear models (tree-based models)
  • Decision tree
  • Random forest
  • XGBoost
  • Model evaluation
  • Regression: Mean Squared Error, Mean Absolute Error, MAPE
  • Classification: Accuracy, Precision-Recall, F1 Score, ROC Curve, Confusion matrix
  • Hyperparameter tuning: GridSearchCV, RandomSearchCV
  • Unsupervised: K means, Hierarchical clustering, Dimensionality reduction (PCA)

  Learning Resources:
  Track A(Free)
  YouTube playlist (more than 2 million views) - First 21 videos
  Feature engineering playlist
  
  Track B (Affordable Fees)
  AI Bootcamp

  Core/Soft Skills:
  Project Management
  Scrum: https://scrumtrainingseries.com/
  Kanban: https://youtu.be/jf0tlbt9lx0
  Tools: JIRA, Notion

  Hands-on Practice:
  Complete all exercises in ML playlist: https://bit.ly/3io5qqX
  Work on 2 Kaggle ML notebooks
  Write 2 LinkedIn posts on whatever you have learnt in ML
  Discord: Help people with at least 10 answers
  Track B: Finish exercises and quizzes for relevant topics

  5. Advanced Topics (Weeks 19-32)

  Week 19: ML Ops – Bridging the Gap Between ML & Deployment

  Take your ML models to production with MLOps.

  Topics Covered:

  • What is ML Ops? Experiment Tracking with MLFlow
  • What is API? FastAPI for Python server development
  • DevOps Fundamentals: CI/CD pipelines, containerization (Docker, Kubernetes)
  • Familiarity with at least one cloud platform (AWS, Azure etc.)

  Learning Resources:
  Track A:
  What is ML Ops
  FastAPI tutorial
  MLFlow Tutorial
  Docker Tutorial
  
  Track B:
  AI Bootcamp

  Weeks 20-21: End-to-End Machine Learning Projects

  You need to finish two end to end ML projects. One on Regression, the other on Classification

  Projects:
  Regression: Bangalore Property Price Prediction
  Classification: Log Classification System
  
  ATS Resume Preparation:
  Here is the resume tips video along with some templates you can use for your data analyst resume: https://www.youtube.com/watch?v=buQSI8NLOMw
  Use this checklist to ensure you have the right ATS Resume: Check here
  Resumes are dying but not dead yet. Focus more on online presence.

  Portfolio Building:
  You need a portfolio website in 2025. You can build your portfolio by using these free resources.
  GitHub: Upload your projects with code on github and using github.io create a portfolio website
  Linktree: Helpful to add multiple links in one page.

  Hands-on Practice:
  Assignment In above two projects make following changes:
  - Use FastAPI instead of flask.
  - Regression project: Instead of property prediction, take any other project of your interest from Kaggle for regression
  - Classification project: Instead of log classification, take any classification project using a Kaggle dataset and build end to end solution along with deployment to AWS or Azure
  Add a link of your projects in your resume and LinkedIn. (Tag Codebasics, Dhaval Patel and Hemanand Vadivel with the hashtag #dsroadmap25 so we can engage to increase your visibility)

  Weeks 22-25: Deep Learning with Neural Networks

  Learn neural networks, CNNs, RNNs, and transformers.

  Learning Resources:
  Track A(Free):
  Deep Learning playlist (tensorflow)
  End to end potato disease classification project
  
  Track B (Affordable Fees):
  AI Bootcamp

  Hands-on Practice:
  - Instead of potato plant images use tomato plant images or some other image classification dataset.
  - Deploy to Azure instead of GCP.
  - Create a presentation as if you are presenting to stakeholders and upload video presentation on LinkedIn.

  Weeks 26-28: Specialization – NLP or Computer Vision

  Many AI engineers choose a specialized track which is either NLP or Computer vision.You don't need to learn both. Choose a domain specialization and master it.

  Natural Language Processing (NLP)
  NLP Topics:
  Regex, TF-IDF, Word2Vec, Naïve Bayes, Spacy
  Learning Resources:
  NLP Playlist

  Computer Vision (CV)
  Basic image processing techniques: Filtering, Edge Detection, Image Scaling, Rotation
  Library to use: OpenCV
  Convolutional Neural Networks (CNN) – Already covered in deep learning.
  Data preprocessing, augmentation – Already covered in deep learning

  Hands-on Practice:
  NLP Track: Complete exercises in this playlist: https://bit.ly/3XnjfEZ

  Week 29-30: Generative AI (Gen AI) and AI Agents

  Explore modern AI advancements with LLMs and AI agents.

  Topics Covered:

  • LLMs, vector databases, embeddings, Retrieval-Augmented Generation (RAG)
  • LangChain framework, AI Agents

  Learning Resources:
  Gen AI Crash Course
  AI Agents Tutorial

  Week 31-32: Gen AI Projects

  Apply Gen AI concepts to solve real-world problems.

  Learning Resource:
  Gen AI project playlist

  Hands-on Practice:
  Implement LLM-based projects
  Build AI agents for automation

  6. Specialization and Career Preparation

  Weeks 33 Onwards:
  - More projects
  - Online brand building through LinkedIn, Kaggle, Discord, Opensource contribution
  - Job application and Success

  Tips for Effective Learning:
  - Spend less time in consuming information, and more time in Digesting, Implementing, Sharing
  - Group learning: Use the partner-and-group-finder channel on the Codebasics discord server for group study and hold each other accountable for the progress of your study plan.

  7. Conclusion: The Future of AI Engineering in 2025

  The AI Engineer Roadmap 2025 provides a clear path to success in the ever-evolving AI field. By following this roadmap, staying disciplined, and utilizing the wealth of free resources available, you can become a highly skilled AI engineer within a year.

  Remember, the journey doesn't stop here. AI is a fast-paced field, and continuous learning is key to staying ahead of the curve. As AI continues to transform industries, the opportunities for qualified engineers will only grow. The time to start your journey toward becoming an AI engineer is now.