Skills Required for AI Engineer Fresher in 2026: A 10-Week Free Roadmap

What does an AI engineer actually do in 2026?

AI engineers in 2026 don't build models from scratch. They apply pre-trained models to build product features. For example, an AI engineer at a food delivery app might plug a pre-trained image model into the app so users can photograph their meal and get a calorie estimate. They do not invent new models. This is the single most important fact for a fresher to understand, because it tells you exactly where to spend your study time.

The AI Engineer Roadmap from roadmap.sh is clear on this split. An AI engineer uses AI to solve product problems: connecting a language model to a chatbot, adding image search to an app, or building a recommendation system on top of an existing API. An AI researcher invents new theory. An ML engineer trains custom models on company data. The AI engineer sits between them, turning available AI into working features.

This distinction changes your study plan completely. You do not need to derive backpropagation from first principles. You do not need a PhD-level understanding of transformers. You need to be good enough at Python to build things, comfortable with ML concepts, and able to use tools like LangChain or Hugging Face to ship a working demo.

What is the difference between an AI engineer, ML engineer, and GenAI engineer?

ML engineers train custom models, GenAI engineers build on foundation models, and AI engineers use AI tools to solve product problems. In India, GenAI roles pay a 10–20% premium over ML roles at mid-level, with GenAI salaries between ₹28–60 LPA and ML salaries between ₹25–55 LPA, according to learnersink's 2026 comparison.

The three roles overlap, but the daily work differs. A table makes the split easy to see, based on the roadmap.sh and learnersink sources:

As a fresher, you are not choosing a permanent lane yet. The roadmap below builds the shared foundation. After that, you can steer toward GenAI tools (LangChain, RAG) or toward ML deployment (PyTorch, MLOps) based on what you enjoy. The roadmap.sh page and the learnersink comparison both agree that the foundational skills are the same.

What are the 5 skills you will build?

This roadmap builds five concrete skills. Each week maps to one of them.

1. Python — Weeks 1–2 via Kaggle Learn.
2. Pandas and data handling — Weeks 1–2 via Kaggle Learn.
3. ML fundamentals — Weeks 3–5 via Google's MLCC.
4. Deep learning — Weeks 6–10 via your chosen track.
5. Project deployment — Week 11+ via Kaggle competition and GitHub.

Start with Kaggle Learn: Python and ML basics

Kaggle's browser-only courses teach Python and intro ML in about 9 hours (faceprep guide, https://faceprep.in/article/free-ai-learning-resources-for-indian-engineering-students-2026/), with no setup and no cost. This is the fastest on-ramp for an Indian student, because there is nothing to install and nothing to pay for.

• Weeks 1–2: Finish Kaggle's Python course, then Intro to Machine Learning. Both run entirely in your browser on Kaggle's servers, so a basic laptop and an internet connection are all you need (faceprep).
• What you will know: Python syntax, pandas for data, and the core ML loop: load data, train a model, evaluate it. That is the foundation everything else builds on.
• Time per day: About two weeks of daily hours is enough. The courses are short, interactive, and designed for complete beginners.

The faceprep guide to free AI learning resources for Indian students lists Kaggle Learn as the starting point, and it is easy to see why. Kaggle's intro ML course gets you to your first trained model within the first hour. You will see results fast, which keeps motivation high during the hardest part of any learning curve.

One caution: resist the urge to skip ahead. Python fundamentals are the difference between understanding AI code and just copying it. If you cannot read a pandas DataFrame or write a simple loop without help, you are not ready for the next stage. Our 90-day Python plan covers those basics first.

How can Google's Machine Learning Crash Course add structure?

Google MLCC's 25 modules take about 15 hours and need no sign-in, giving you solid ML theory with exercises. This is the stage where you learn why the models work, not just how to call them.

After MLCC, you'll know why a model overfits and how to fix it with regularization. You'll also understand how validation sets keep your model honest and why feature engineering turns raw data into useful inputs.

The faceprep resource list recommends this course right after Kaggle for good reason. Kaggle teaches you the moves. MLCC teaches you the concepts underneath those moves. You will learn what a hyperparameter is, why you split data into train and test sets, and what the numbers in a training log actually mean.

This stage takes about three weeks at an hour a day: weeks 3–5 of the roadmap. No sign-in means no barriers. Just open the page and start. When you finish, you will understand at a conceptual level what the deep learning courses in the next stage are doing.

Which deep learning track should you pick: NPTEL, fast.ai, or Hugging Face?

In weeks 6–10, choose NPTEL Deep Learning (IIT Madras) for rigour, fast.ai for hands-on coding, or the Hugging Face course for NLP and LLMs. You do only one of these, and the choice depends on your goal.

Here is the comparison, based on the faceprep guide to free AI resources:

NPTEL runs through SWAYAM, India's official platform for free university-level content. Lectures, assignments, and discussion forums are all free, delivered by IIT and IISc faculty. The exam certificate costs about ₹1,000 and is optional. The project you build matters more, as the faceprep guide stresses.

If your goal is a GenAI role, the Hugging Face course is the direct path, since it teaches the transformer architecture and the tools you will actually use at work. If you want a traditional ML foundation with academic depth, NPTEL is the stronger choice. If you learn by doing and want to see results tonight, fast.ai wins. Pick one and commit for five weeks.

Should you build one project and enter one Kaggle competition?

In week 11+, do one Kaggle competition and build one GitHub project you can demo. This matters more than any certificate, and it is the step that gets you interviews.

• Competition: Enter one active Kaggle competition with a simple dataset. Even a modest leaderboard position teaches you feature engineering, validation, and iteration under a deadline (faceprep).
• Choose your project: Pick something you can explain in two minutes: a spam detector, a movie recommender, a chatbot for a niche topic. Small and finished beats large and abandoned.
• Make it demo-ready: Push code to GitHub with a clear README, a sample input, and a screenshot of the output. Run it live on Streamlit or a free tier so a recruiter can click and see it work.
• Write two paragraphs: In your resume, explain the problem, your approach, and one number that shows it works, such as accuracy or response time in words.

A recruiter cannot run your NPTEL certificate through a test. They can open your GitHub link and click the demo. The faceprep guide is blunt on this point: the project matters more than the certificate. A working app that solves a real problem is evidence you can do the job. A certificate only proves you sat through a course. For a first project, try our step-by-step guide to building an AI agent in Python.

Your Kaggle competition also gives you something to talk about in interviews. You can describe what you tried, what failed, and what you changed. That story is far more convincing than reciting definitions from a course.

Are free cloud GPUs on Colab and Kaggle enough?

Google Colab gives you a T4 GPU for a few hours per session, and Kaggle gives you 30 hours per week. That is enough to train everything in this roadmap.

You do not need to buy a laptop with a dedicated GPU, and you do not need to rent cloud compute. The faceprep guide confirms that free tiers cover this entire learning path.

Colab's T4 is sufficient for the deep learning course exercises and a smaller Kaggle competition. Kaggle Notebooks give you a T4 or P100 with a 30-hour weekly limit, which is generous for a student working an hour or two a day. Save your notebooks to your account so your work persists between sessions.

One practical tip: develop on a small sample of your data first, test the code quickly, then run the full training when you know it works. This saves you from burning your free hours on debugging runs that crash in the first five minutes.

Do you need the NPTEL certificate?

The faceprep resource guide is explicit: the certificate is optional, and the project is what matters. If ₹1,000 is a lot for you, skip the exam without guilt. Spend that time and money on your GitHub project instead.

If you do take the NPTEL course, here is how to talk about it on your resume honestly:

• List the course as: Deep Learning, NPTEL (IIT Madras). If you didn't take the exam, write "completed" not "certified."
• Do not claim exam certification if you did not take the exam. Say "course completed" instead.
• Link your project as the proof of what you learned from the course.

The certificate is not a lie, but it is also not the thing that lands the job. Your demo is.

How does this roadmap get you an interview?

AI-skilled graduates made up 60% of TCS's fresher hires in FY26 (faceprep), and your staged project shows you can apply AI practically, which is what employers actually check. The market is paying for applied skill, not for paper.

That 60% figure comes from TCS's CHRO at the AI Impact Summit in March 2026, as reported by faceprep. It tells you that Indian employers are already shifting their hiring toward candidates who can use AI, not just those who studied it in college. Whether you are aiming at a TCS-level campus placement or a startup role, the same evidence applies: a candidate who can show a working AI project beats one with only theory.

The roles are out there. If you are curious about entry-level AI-adjacent positions, DevHives keeps a running list of companies hiring freshers in India, and an overview of entry-level tech roles for 2026. For the broader picture of how AI engineering fits into the software world, this comparison of software engineering versus prompt engineering clarifies the different career paths.

The roadmap is ten weeks of steady work, an hour a day, with zero rupee spent on courses. In that time you will move from knowing nothing about AI to holding a demo a recruiter can click. That is the entire job search advantage. Everything else, including certificates, is secondary. Open Kaggle Learn today.