AI Engineer Roadmap: Your Week-by-Week Guide to Landing a Job in 2025

Introduction

The AI revolution is here, and companies are pouring billions into AI projects. What does this mean for you? HUGE opportunity! If you’re looking for a career that’s not only in high demand but also incredibly rewarding, becoming an AI Engineer might be your calling. But where do you start? This roadmap is your comprehensive, week-by-week study plan, packed with free learning resources and checklists, to help you become a sought-after AI Engineer. Ready to jump in?

Is AI Engineering Right for You?

Before diving headfirst, let’s get real. Is AI Engineering truly the path for you? It’s essential to evaluate your interests and skills honestly. Do you enjoy coding? Are you comfortable with math? AI jobs demand strong capabilities in both areas. If coding and math aren’t your thing, that’s perfectly okay! The AI field is vast, offering exciting roles such as AI Sales Representative, AI Product Manager, or AI Ethics Executive.

Not every role requires you to be a coding whiz. There are other paths into this exciting industry! It’s all about finding where your strengths and interests align with the opportunities available.

Week 0: Avoiding Scams and Finding the Right Resources

Hold on! Before you start signing up for courses, let’s address something crucial: scams. The online learning world can be tricky, with many claiming to be experts. Do your homework! It’s important to learn from instructors with solid industry experience. Otherwise, you might waste time and money on ineffective courses.

Remember, if it sounds too good to be true, it probably is! Check the background and credentials of instructors. Real experience speaks volumes.

Weeks 1 & 2: Computer Science Fundamentals

Let’s get foundational! A strong understanding of computer science principles is non-negotiable. If you have a computer science degree, you’re already ahead. But don’t worry if you don’t. Khan Academy to the rescue!

This free course covers the essentials, like bits and bytes, storing text and numbers, computer networks, and programming basics. Focus on the first four modules to build a solid base:

• Bits and Bytes
• Storing Text and Numbers
• Basic Computer Networks
• HTTP and the World Wide Web
• Basics of Programming

Think of it this way: Data Scientist + Software Engineer = AI Engineer. Solid software engineering fundamentals are the key!

Weeks 3 & 4: Python Basics

Python is the language of AI! Learning Python is surprisingly accessible. Start with the basics, and you’ll be amazed at how quickly you progress. Two fantastic YouTube playlists can guide you: the Codebasics’ Python playlist and Corey Schafer’s Python playlist.

Focus on the first 16 tutorials to grasp the core concepts. Then, put your knowledge to the test! Complete all the exercises. It’s tempting to peek at the solutions, but resist! Struggle through them independently first. It’s where the real learning happens.

Don’t forget soft skills! Start building your LinkedIn profile now. LinkedIn is your ticket to landing that dream AI job. Don’t wait until you’re a technical genius; start building your professional brand today.

LinkedIn Profile Checklist

Your LinkedIn profile is your online handshake. Make it count! Here’s a checklist to ensure your profile shines:

• A professional profile picture
• A compelling headline
• A summary that tells your story
• Detailed experience descriptions
• Skills and endorsements
• Recommendations from colleagues

Tick off each item as you complete it. A strong LinkedIn profile dramatically increases your chances of getting noticed.

Weeks 5 & 6: Data Structures and Algorithms

Time to level up! As an AI Engineer, you’ll write programs that need to handle massive amounts of data efficiently. Understanding data structures and algorithms is crucial. Dive into this free YouTube playlist on Data Structures and Algorithms.

Learn the trade-offs between memory and CPU usage. Practice the exercises diligently to reinforce your understanding. This journey can be long, so stay inspired! Watch interviews with people who’ve successfully transitioned into AI, like Tanul Singh, a mechanical engineer who became an ML Engineer using Kaggle.

Weeks 7 & 8: Advanced Python

Ready for more Python? Now, explore advanced concepts like inheritance, generators, iterators, list comprehensions, multi-threading, and multi-processing. These are essential for writing scalable, enterprise-level programs.

These concepts might sound scary but they’re all about optimizing your code for efficiency. For example, when dealing with huge datasets, generators and iterators allow you to process data on-the-fly, without loading everything into memory at once. Refer back to the same Python playlist, focusing on videos 17-27. Complete the exercises!

Soft Skills: AI Influencers and Business Fundamentals

Tech skills alone aren’t enough. Cultivate your soft skills too! Start following prominent AI influencers on LinkedIn. People like Nitin (Head of AI Services at Google) and Dalana share valuable insights on current trends and hiring practices.

Engage with their posts! Share your thoughts, ask questions, and build relationships. Remember, your online presence is your new resume. But avoid generic comments like “True!” Add value!

Understanding business concepts is also crucial. As an AI Engineer, you’ll be working on projects that impact business goals. Check out the Think School YouTube channel for insightful business case studies.

The Art of Asking Questions

Got questions? Of course, you do! Learning AI is challenging. Discord is your friend! Join AI-related Discord servers like the Codebasics Discord server to ask questions and get help from the community.

But here’s the secret: ask questions the right way. Don’t just copy-paste error messages. Explain what you’ve tried and what you’re struggling with. Seek guidance, not spoon-feeding.

Assignment & Progress Tracking

Time to put it all together!

• Write meaningful comments on at least 10 AI-related LinkedIn posts.
• Note key learnings from three Think School case studies and share them.

Track your progress! Mark assignments as complete as you go. It’s a great way to see how far you’ve come and stay motivated.

Weeks 9 & 10: Version Control Systems

Collaboration is key in AI. Understanding version control systems like Git and GitHub is essential for working on team projects. Learn the basics:

• Committing
• Branching
• Pull Requests
• Merging
• Resolving Conflicts

Refer to these YouTube playlists: Corey Schafer’s Git Playlist and Codebasics’ Git Playlist. Also, be sure to check out this interview with Mahad, a mechanical engineer who became a deep learning engineer through self-study.

Presentation Skills: Death by PowerPoint

Don’t underestimate the power of a good presentation! As an AI Engineer, you’ll need to communicate complex ideas to stakeholders. Watch the “Death by PowerPoint” video for tips on creating effective presentations. Here’s one from Don McMillan.

Weeks 10 & 11: SQL and Relational Databases

Data is the fuel of AI. SQL (Structured Query Language) is how you access and manipulate that data. Learn these key SQL topics:

• SELECT statements
• WHERE clause
• JOINs
• GROUP BY
• Aggregate functions

Khan Academy offers an excellent free SQL course. Also, check out W3 Schools and SQL Bolt.

Consider participating in the SQL resume project challenge on the Codebasics platform. Aran Sharma landed a job as a data analyst based on his participation!

This week was when things started to feel real. Up until now, I was learning the theory and getting excited about AI… but NumPy and Pandas? These are the tools that actually let you do stuff with data.

If you’re working with any kind of data — CSV files, Excel sheets, database exports, you name it — you’re going to be using these two libraries all the time.

Week 12: Mastering NumPy and Pandas – Your First Real Data Tools

This week was when things started to feel real. Up until now, I was learning the theory and getting excited about AI… but NumPy and Pandas? These are the tools that actually let you do stuff with data.

If you’re working with any kind of data — CSV files, Excel sheets, database exports, you name it — you’re going to be using these two libraries all the time.

🧮 NumPy: The Foundation of Fast Math in Python

NumPy (short for Numerical Python) is where everything starts. It’s all about arrays — fast, efficient, multi-dimensional arrays.

Here’s what I focused on:

• Creating arrays manually and from lists

• Array slicing and indexing (super useful for selecting specific parts of your data)

• Broadcasting (a fancy word that means doing operations across arrays easily)

• Basic math: mean, sum, max, min, etc.

Example: I took a list of temperatures and used NumPy to normalize them — that’s something most machine learning models need.

Once I got the hang of arrays, it felt like my Python skills leveled up instantly.

🐼 Pandas: Your Personal Data Assistant

If NumPy is raw power, Pandas is smart power. It’s built on top of NumPy and makes working with structured data (like tables) super intuitive.

Here’s what I practiced:

• Reading in data from .csv and .xlsx files

• Exploring DataFrames: .head(), .info(), .describe() — the usual suspects

• Selecting and filtering rows/columns

• Handling missing values (drop, fill, or impute)

• Grouping and aggregating data (like finding average revenue by region)

• Merging and joining multiple DataFrames

Real-life moment: I took a messy Excel file I downloaded from Kaggle and cleaned it into something actually usable — just using Pandas. It felt like magic.

🛠️ What Helped Me Learn Faster

• Practice Notebooks: I created mini-projects, like analyzing movie ratings or cleaning up a restaurant dataset.

• Kaggle: I’d download a random dataset and give myself 30 minutes to explore it using only Pandas.

• YouTube: There are so many beginner-friendly Pandas/NumPy crash courses. I liked the ones by freeCodeCamp and Data School.

  

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Weeks 13-17: Math and Statistics for AI

Time for the heavy lifting! This week is all about laying down the bedrock of AI: math and statistics. I used to think I could skip this part — just use libraries and pre-built models — but trust me, once you start working on real projects, you realize how important it is to actually understand what’s happening under the hood.

If you’re serious about AI, this isn’t optional. This is where your intuition is built.

📚 What I’m Focusing On:

🔹 Linear Algebra

Everything in ML — from datasets to model weights — is basically a matrix at some point. Learn about:

• Vectors & matrices

• Matrix multiplication

• Eigenvalues and eigenvectors (used in PCA, neural nets, and more!)

Real-world link: Ever wondered how facial recognition compresses faces into features? That’s eigenfaces — powered by linear algebra.

🔹 Calculus

It’s not about solving equations on paper — it’s about understanding how models learn.
Focus on:

• Derivatives and gradients (used in backpropagation)

• Chain rule (helps neural nets learn through layers)

Tip: You don’t need to be a math genius — just enough to understand why gradient descent works.

🔹 Probability

AI is all about uncertainty and decision-making. Probability helps with:

• Making predictions

• Understanding models like Naive Bayes

• Working with random variables and distributions

Example: In fraud detection, probability helps a model decide whether a transaction is “likely” or “unlikely” to be fraudulent.

🔹 Statistics

Statistics helps you make sense of data and evaluate models.

• Mean, median, mode, standard deviation

• Hypothesis testing

• Confidence intervals

• Correlation vs causation

Why it matters: You can’t judge a model unless you understand metrics like precision, recall, F1 score — all of which are rooted in statistics.

💡 Learning Resources That Actually Helped Me:

• 📘 Khan Academy: A lifesaver. It breaks down math concepts in a friendly, digestible way. Great if you’re brushing up after a break (like I was).

• 🎥 3Blue1Brown YouTube Channel: Seriously one of the best things on the internet. Their visual explanations of linear algebra and neural networks are mind-blowing. You’ll never look at matrices the same way again.

🔁 I usually watch a 3Blue1Brown video before starting a topic, and use Khan Academy to go deeper with exercises and theory.

🛠️ How I’m Practicing

• Creating small notebooks with visual demos: matrix operations, plotting functions, gradients, etc.

• Applying stats to real-world data (like calculating skewness and kurtosis in a dataset)

• Building intuition over memorization — because I want to understand, not just pass a quiz.

Week 18: Exploratory Data Analysis

This week was all about getting my hands dirty with real data — not perfectly clean, not pre-labeled, not simplified for tutorials. I dove into Exploratory Data Analysis (EDA) — and let me tell you, it’s one of the most underrated but powerful skills in the entire machine learning pipeline.

If machine learning is the engine, EDA is the roadmap. It helps you understand where you’re starting, what direction to go, and which bumps in the road you need to smooth out before you can even think about modeling.

🧹 What is EDA, Really?

EDA is where you:

• Inspect the data: What are the columns? Are there missing values? What’s the data type?

• Clean it up: Fix or remove bad values, duplicates, and inconsistencies.

• Transform it: Normalize values, encode categorical variables, create new features.

• Visualize patterns: Use graphs to spot relationships, trends, and anomalies.

Basically, you’re telling the story of the data — before any modeling happens.

🧰 Tools I Used

Here’s my go-to stack for EDA this week:

• NumPy – for number crunching and arrays.

• Pandas – for exploring rows, columns, nulls, and doing quick transformations.

• Matplotlib + Seaborn – for visualizations like histograms, pair plots, heatmaps, box plots, etc.

Each of these tools brings something different to the table — and when you combine them, you’re unstoppable.

📁 Where I Practiced (Kaggle FTW)

I found Kaggle to be a goldmine. The community, the datasets, and the kernels (notebooks) shared by others gave me tons of inspiration.
Here are the datasets I practiced on:

🧪 First Three:

1. Titanic Dataset – A classic! Great for learning how to deal with missing values and categorical variables.

2. Netflix Movies and Shows – Perfect for string cleaning, date parsing, and multi-label analysis.

3. IPL Cricket Matches – Super interesting! Had fun finding player performance trends and season-wise changes.

🔁 Then Two More:

4. Zomato Restaurants – Focused on cleaning up messy categorical data and mapping cities.

5. US Accidents (2016–2021) – Larger dataset, great for dealing with real-world messiness and geolocation.

Each one taught me something new. For example, the IPL dataset made me realize the importance of groupby analysis, and the Zomato data helped me understand text normalization and duplicates.

🔍 Key Takeaways

• EDA isn’t just about pretty visuals — it’s how you understand your problem.

• You’ll always find surprises: weird outliers, columns that don’t make sense, or patterns you didn’t expect.

• Even a simple histogram can tell you a lot about the distribution and data quality.

• Don’t rush to model — take the time to understand the data. It pays off later.

🚀 What’s Next

• Explore feature engineering based on what I learned during EDA.

• Build small dashboards to summarize insights from datasets (maybe using Plotly or Dash).

  

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• Start getting into correlation vs causation thinking — what’s truly impactful?

Weeks 18-21: Machine Learning

This week, I’m diving deep into the core of AI: statistical machine learning. If you’ve ever wondered what really powers AI beyond the buzzwords, this is it. It’s not just about fancy neural networks — it’s about the math and statistical foundations behind every prediction, recommendation, or classification we see in the real world.

📊 Why Statistical Machine Learning Matters

At its core, machine learning is about finding patterns in data — and that means statistics. Understanding concepts like probability distributions, Bayes’ theorem, maximum likelihood estimation, and bias-variance tradeoff will make you a much stronger ML practitioner.

This is the kind of knowledge that helps you not just use algorithms, but truly understand them.

🎓 What I’m Watching This Week

I found an excellent YouTube playlist by Krish Naik, which covers statistical ML techniques in a practical, beginner-friendly way. It’s hands-on, to the point, and packed with industry-relevant content:

📺 Statistical Machine Learning Playlist – by Krish Naik

I’m personally following along and pausing often to take notes and try the code on my own. Highly recommend if you want to build a solid foundation!

🔄 Agile: Working Like the Pros

Apart from the ML side, I also started looking into Agile project management, specifically Scrum and Kanban. Why? Because no matter how good your ML models are, if you can’t manage projects, track progress, or work in a team efficiently, you’ll struggle in real-world jobs.

A quick breakdown:

• Scrum = short, focused sprints with planning and review meetings

• Kanban = visual boards to manage tasks, progress, and bottlenecks

I tested out Trello and Jira this week just to get a feel. Even managing a personal ML project using a Kanban board made things feel way more organized.

💬 Final Thoughts

This week’s been all about leveling up both the brains and the workflow behind AI. Learning how algorithms work and how to manage ML projects is such a powerful combo.

If you’re on the same path, take this week to:

• Strengthen your statistical ML knowledge

• Watch the full YouTube playlist

• Try managing your tasks in Trello or a Kanban board

• Read a bit about Scrum ceremonies (daily standups, retrospectives, etc.)

Every bit of this adds up to becoming a well-rounded, job-ready AI pro. Keep pushing. You’ve got this. 👊📈💡

Week 22: MLOps

This week I stepped into the world of MLOps, and honestly, it feels like a whole new universe. Up until now, I’ve been focused on building models — cleaning data, training algorithms, tuning hyperparameters… all the classic stuff. But then it hit me: what happens after I’ve built the model?

That’s where MLOps comes in. And wow — it’s a game-changer.

🛠️ So, What is MLOps Anyway?

Think of MLOps as the missing link between your model and the real world. You can build the smartest model ever, but if you can’t deploy it, monitor it, or update it easily, it’s just… sitting there.

The way I see it, MLOps is like DevOps, but for machine learning. It’s about taking your model out of the Jupyter notebook and making it usable by actual people, apps, or systems.

👨‍💻 What I Focused on This Week:

🔸 Building an API

I started small by creating a Flask API for one of my older regression models. Just being able to send a request and get a prediction back felt like magic at first. It was a simple use case — predicting house prices — but turning it into an API made it feel real.

🔸 Docker

Then came Docker. At first, I was like, “Why do I need this?” But once I packaged my API into a Docker container and ran it without any setup on another machine, it clicked. This is how you make sure your model works anywhere — locally, on the cloud, or on someone else’s computer.

🔸 Cloud Platforms & SageMaker

I also dipped my toes into Amazon SageMaker — and wow, it’s powerful. I trained a model directly on SageMaker using their built-in Jupyter environment and deployed it to a real endpoint in a few clicks. Super cool (though the AWS console can feel like a maze at first). It’s definitely something I’ll be exploring more.

🔸 Kubernetes (Just a Peek)

I only briefly looked at Kubernetes this week — it seems like the next level for managing and scaling ML models in production. Not rushing it, but I wanted to at least understand the basics: pods, nodes, services, etc.

🧠 What I Learned (So Far)

• Deploying a model is just as important as building it

• Tools like Docker and Flask are essential building blocks

• Cloud platforms make deployment easier — but they come with a learning curve

• MLOps isn’t just technical — it’s strategic (versioning, monitoring, rollback plans, etc.)

And most importantly: your model doesn’t matter if no one can use it.

🚀 What’s Next for Me

I want to:

• Try deploying a FastAPI project next

• Explore more of SageMaker’s automation features

• Read up on model monitoring and retraining pipelines

• Maybe try out GCP or Azure for comparison

Final Thoughts

MLOps felt intimidating at first, but now I’m excited. It’s like I’ve finally stepped into the part of machine learning where the rubber meets the road. This is the kind of stuff that makes you job-ready and shows you can actually ship things.

If you’re in the same spot — learning ML but not sure what’s next — start looking into MLOps. It’s a bit of a learning curve, sure, but it’s so worth it.

You’re not just building models.
You’re building solutions.

Let’s keep going. 👊

Weeks 23-24: Building Machine Learning Projects

You’ve spent weeks learning the theory, practicing the code, and wrapping your head around all kinds of algorithms — now it’s time to put all of that into action. Over the next two weeks, your focus will shift to something incredibly valuable and exciting: building real, end-to-end machine learning projects from scratch.

🔨 The Goal: Two End-to-End Projects

You’ll work on two full ML projects, one for each major type of supervised learning:

1. Regression Project – where the output is a continuous value (like predicting prices or sales)

2. Classification Project – where the output is a category or class (like spam vs. not spam, or cat vs. dog)

📺 What You Need to Do:

Pick two YouTube playlists, one for each project type, that walk through a full project from start to finish. These playlists should cover:

• Understanding the problem

• Loading and exploring the dataset

• Data cleaning and preprocessing

• Choosing the right model

• Training and tuning the model

• Evaluating performance

• (Optional but awesome): Model deployment or visualization

Make sure to code along, don’t just watch passively. If something doesn’t make sense, pause the video, take notes, or dig into the documentation. This is about doing, not just observing.

💡 Some Example Projects You Could Look For:

🔹 Regression Project Ideas:

• Predicting house prices using the California Housing Dataset

  

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• Estimating car resale values based on mileage, age, and condition

• Forecasting sales or revenue for a business using time series data

🔹 Classification Project Ideas:

• Classifying emails as spam or not spam

• Predicting whether a customer will churn based on usage data

• Identifying handwritten digits using the MNIST dataset

• Detecting fraudulent credit card transactions

🔍 Tips for Choosing Playlists:

• Look for playlists that are beginner-friendly but still go deep enough to show you the whole workflow.

• It’s a bonus if the creator explains why they’re making certain decisions (like choosing a specific model or preprocessing technique).

• Channels like Krish Naik, CodeBasics, Simplilearn, freeCodeCamp, and Ken Jee often have solid tutorials.

🎯 Why This Matters:

Working on actual projects is the best way to reinforce your learning. It helps you:

• Learn to think like a data scientist or ML engineer

• Understand real-world problems and how messy real data can be

• Build your portfolio (perfect for resumes, LinkedIn, or GitHub)

• Gain confidence in your ability to create something meaningful

📝 Bonus Tip: Document Everything!

• Create a GitHub repo for each project

• Write a simple README explaining what the project does, the tools used, and how to run it

• Take notes on what you learned, where you struggled, and how you solved those challenges

This documentation is gold when you’re preparing for interviews or sharing your work with others.

🚀 Let’s Go!

By the end of Week 24, you’ll have two awesome machine learning projects under your belt. Not only will you have hands-on experience, but you’ll also be that much closer to being ready for internships, freelance gigs, or full-time roles.

You’re building real things now. Keep going — this is where it gets fun. 🎉🔥

Portfolio Website Assignment and Example

👉 Related Guide: Want to build apps without coding? Check out our step-by-step tutorial on How to Build a No-Code App Using AI!

Create your own project portfolio website to showcase your skills.

• List your skills
• Detail the projects you’ve worked on
• Provide links to GitHub or other online tools where you showcase your work

And make sure your resume is optimized for Application Tracking Systems (ATS). It’s a MUST for getting past the initial screening.

Weeks 25-27: Deep Learning

The buzz is real! Deep learning is driving innovations like LLMs and ChatGPT. Delve into neural networks, convolutional neural networks, and sequence models.

Weeks 28-30: NLP or Computer Vision

Specialize! Focus on either Natural Language Processing (NLP) or Computer Vision.

Weeks 31-32: LLM and LangChain

Get ready for the future! LLMs (Large Language Models) and LangChain are hot right now. If you are looking for a job, learning this framework is a must.

Week 33+: Continuous Learning and Job Application

The journey never ends! AI is constantly evolving. Keep learning, keep building, and keep applying. With dedication and the right skills, you’ll land that dream AI Engineer job.

Tips for Effective Learning

• Consume less, digest more. Spend less time watching tutorials and more time implementing and experimenting.
• Join a study group. Learning with others keeps you motivated and accountable.

AI Engineer Roadmap: FAQ

1. What skills do I need to become an AI engineer in 2025?

   • You need programming skills (Python, TensorFlow, PyTorch), knowledge of machine learning, deep learning, data structures, and cloud computing.

2. How long does it take to become an AI engineer?

   • It depends on your background. If you have programming experience, it can take 6-12 months of dedicated learning. If you’re starting from scratch, it may take 1-2 years.

3. Do I need a degree to get an AI engineering job?

   • No, but having a degree in computer science, AI, or data science helps. Many companies accept self-taught candidates with strong portfolios and certifications.

4. Which programming languages should I learn for AI?

   • Python is the most popular, but R, Java, and C++ are also useful.

5. What are the best AI courses and certifications?

   • Some top options include:

     • Google Machine Learning Crash Course (Free)

     • Deep Learning Specialization by Andrew Ng (Coursera)

     • IBM AI Engineering Professional Certificate (Coursera)

     • TensorFlow Developer Certification

6. How do I build a portfolio as an AI engineer?

   • Work on real-world projects, contribute to open-source projects, and showcase your work on GitHub, Kaggle, or a personal blog.

7. What are the best AI tools and frameworks to learn?

   • TensorFlow, PyTorch, Scikit-learn, OpenCV, and Hugging Face Transformers are widely used in AI engineering.

8. Can I become an AI engineer without coding?

   • No, coding is essential for AI engineering. However, there are no-code AI platforms like Google AutoML and Teachable Machine for non-programmers.

9. What industries are hiring AI engineers in 2025?

   • AI engineers are in demand in healthcare, finance, robotics, cybersecurity, autonomous vehicles, and e-commerce.

10. What is the average salary of an AI engineer in 2025?

• Salaries vary by country, but in the U.S., AI engineers earn $120,000–$180,000 per year on average. Experienced engineers can earn over $250,000.

Concluding Remarks

You’ve got this! With hard work, dedication, and this roadmap, you’ll be well on your way to becoming a successful AI Engineer.