Link: https://roadmap.sh/ai-engineer

Hey everyone!

I've just started my ME/MTech in Electronics and Communication Engineering (ECE), and I'm aiming to transition into the role of an AI Engineer within the next 8 to 12 months. I'm starting from scratch but can dedicate 6 to 8 hours a day to learning and building projects. I'm looking for a detailed roadmap, along with project ideas to build along the way, any relevant hackathons, internships, and other opportunities that could help me reach this goal.

If anyone has gone through this journey or is currently on a similar path, I’d love your insights on:

1. Learning roadmap – what should I focus on month by month?

2. Projects – what real-world AI projects can I build to enhance my skills?

3. Hackathons – where can I find hackathons focused on AI/ML?

4. Internships/Opportunities – any advice on where to look for AI-related internships or part-time opportunities?

Any resources, advice, or experience sharing is greatly appreciated. Thanks in advance! 😊

I found this roadmap: https://roadmap.sh/ai-data-scientist. Shall I follow it as it is? Is it good?

For becoming an ML Engineer, should I also follow https://roadmap.sh/devops?

Thanks :)

What is the best complete roadmap for AI, ML, DL, and Data Science?

Some roadmaps I have found (first 2 best?):

• [i.am.ai] AI Expert Roadmap

• [roadmap.sh] AI and Data Scientist Roadmap

• [github.com] mrdbourke/machine-learning-roadmap

• [github.com] luspr/awesome-ml-courses

• [rentry.org] Machine Learning Roadmap

Which one should I choose? I am not a beginner in programming (8y as a hobby and 3y working), but it was not related to AI.

Hi! What is the best complete roadmap for AI, ML, DL, and Data Science?

Some roadmaps I have found:

• [roadmap.sh] AI and Data Scientist Roadmap ← Best?

• [i.am.ai] AI Expert Roadmap

• [github.com] mrdbourke/machine-learning-roadmap

• [github.com] luspr/awesome-ml-courses

• [rentry.org] Machine Learning Roadmap

Also maybe there is something like a "Master in ML" roadmap of courses?

Which one should I choose? I am not a beginner in programming (8y as a hobby and 3y working), but it was not related to AI.

I'm not telling you everything from start to finish, but did anyone try to grab a topic they were interested in and seriously take the time to learn each thing they mention there? Does that make sense? How long should it take?

I'm asking because technology and programming are pretty fun for me, especially when applied to personal projects, but I'm falling into relying too much on AI agents and I feel like I'm not really adding knowledge or skills for my resume (I've been working in systems for 7-8 years but as a QA Automation). How do I know what the right balance is between theory, practice, AI, and ass-in-chair time?

Since FE,BE,DEVOPS are big, each got alot of concepts. so do you use the site to see where you are?

or if there are better ways?

Hey, I want to start learning Python. I tried reading Python Crash Course but it was boring. I tried CS50P but it felt slow and hard to understand. So I just decided to try roadmap.sh advices on resources for each concepts.

Can I rely on roadmap.sh? It is community driven but are these resources chosen by people who checked them or just people who googled and chose top websites form google search?

I’m a sophomore in highschool going into my junior year Fall of 2024. Before I go to college (Fall of 2026) what’s everything I need to know about AI and ML whether it’s programming languages, concepts, etc. A detailed roadmap would be great as I’m a complete beginner that that knows incredibly basic python.

Just last year, I was a software developer mostly creating web applications, working on backend services, APIs, and the regular CRUD operations using Python and JavaScript. Good job, good payment, but I thought I was missing the part of tech that was really thrilling. Currently, I work as an AI Engineer building applications based on LLM and deploying the models. It was a long journey of about 18 months, but it definitely paid off.

 If you are a programmer and think about changing your career path, here is the very same roadmap I utilized. It is hands on, aimed at quickly real stuff and makes use of your present coding abilities which is the major plus of AI engineering as it is 70% software development anyway. No PhD required just keep working on projects and acquire knowledge through practice.

 TIME & COST REALITY CHECK:

Real talk on timeline and cost. I did this over 18 months while working full time about 10 to 15 hours per week on learning and projects.

Months 1-3 → Foundations

Months 4-10 → Core ML and DL + early projects

Months 11-18 → Modern AI, MLOps, portfolio, and job hunting.

Almost everything is free today like YouTube, official docs, Google Colab for GPUs. Self study works great for developers, but if you want structure and accountability, paid options help a lot.

PHASE 1: How Basics are dealt with (1 TO 2 MONTHS):

I already knew Python well, so I skipped the beginner stuff. But if your knowledge of python is not fresh, then spend a week on advanced topics like decorators, async, and virtual environments.

 Then, I dove into the math and ML foundations just enough to not feel lost:

• Linear algebra, probability, and stats by Khan Academy videos + 3Blue1Brown's essence of linear algebra series.

• Andrew Ng's Machine Learning course on Coursera, the classic one, is free and explains things intuitively.

This gave me the "why" behind algorithms without overwhelming me.

 
PHASE 2: CORE MACHINE LEARNING & DEEP LEARNING (2 TO 3 MONTHS):

I went ahead and got my hands dirty with the practical ML:

• Fast ai's Practical Deep Learning course is a really good option. I got to create my own models from the very first day.

• Next, I took Andrew Ng's Deep Learning Specialization which is all about TensorFlow and PyTorch.

 The main libraries I learned were: NumPy, Pandas, Scikit-learn, Matplotlib, and Seaborn for the basics, followed by PyTorch which I took over TensorFlow because it is more Pythonic and dominant in 2025.

 The projects I worked on were simple but very important:

1. Made a movie recommendation system using collaborative filtering on a dataset from Kaggle.

2. Conducted image classification with CNNs on the CIFAR-10 dataset.

3. Performed sentiment analysis of Twitter data using NLP basics with the help of Hugging Face transformers early on.

They were all deployed on Streamlit for quick and easy web demonstrations that are super easy as a developer.

 RESOURCES & COURSES (WHAT ACTUALLY HELPED):

I have such a clear mind about this. I was a full time earnings person. I needed live doubt clearing and project feedback. Watching recorded videos alone wasn’t enough. So here is how I looked at learning options.

Self Study resources:

1. Coursera’s ML Specialization:

Still the best for building strong ML foundations. Clear explanations, no noise.

1. Fast ai:

Completely free and very practical. Helps you build intuition fast 

These are amazing, but they require strong self discipline. I saved money this way, but progress can get slow if you are busy in office. Structured programs are better if you work full time.

3. LogicMojo AI & ML Course : One option personally good for working developers is LogicMojo’s AI & ML program. I feel complex topic like Deeplearning and genAI you can only learn with projects. I feel this course was good for practical based approach for preparation.

A few things that seemed useful for people who needed structure:

It goes from classic ML → Deep Learning → GenAI

•  Strong focus on real projects

• Includes DSA + system thinking.

• Guided prep helps reduce trial and error during job switches

This is just one example similar cohort programs can work if they fit your schedule and learning style.

My honest take, 

Self study = cheaper, flexible, but needs discipline. 

Structured programs = costlier, but keep you consistent and accountable

There is no arguably one "best." Rather, there is a "fit" that attends to and collaborates with the schedule's energy in terms of learning style. The platform becomes inconsequential compared to the consistency.

 PHASE 3: DIVE INTO MODERN AI (3 TO 4 MONTHS):

This is where it got fun and where most AI engineer jobs are in 2025. Traditional ML is table stakes companies want people who can build with LLMs.

Resources:

• LangChain docs and tutorials for chaining models, agents, etc.

• Hugging Face courses on transformers and fine tuning.

•  Pinecone for vector databases.

Projects that leveled me up:

1. A RAG chatbot: Uploaded PDFs, used embeddings + retrieval to answer questions with GPT-3.5 via OpenAI API. Added memory for conversation history.

2. Custom fine tuned model: Took Llama 2 open source, fine tuned on a small dataset for code review.

3. Multi modal app: Built an image captioning + question answering tool with CLIP and BLIP models.

A very clean code GitHub repository with exhaustive README files and demonstrations was the primary reason for recruiters’ positive reaction to access to deployed apps.

PHASE 4: MLOPS, DEPLOYMENT, AND PRODUCTION BASICS (2 MONTHS):

As a developer, this was my superpower. AI folks often struggle with scaling, but I already knew Docker, etc.

Learned:

• FastAPI for building APIs around models.

• Docker basics for containerizing.

• For the purpose of tracking experiments, MLflow or Weights & Biases can be used.

• In terms of cloud deployment, AWS SageMaker or GCP Vertex AI will be the choices.

Project:

1. Took my RAG app, containerized it, added monitoring for token usage, latency, and deployed to AWS. Simulated production issues like rate limits and fallbacks.

MAJOR PROBLEM I FACED:

• Math overload avoids paralysis by proof work in small incremental.Tutorial hell after every course and video, force yourself to build something original even if it is bad at first.

• Skipping deployment early to deploy every project, even simple ones on Streamlit. Production problems teach way more than perfect Jupyter notebooks.

• Burnout I only did deep work on weekends and evenings. Set small weekly goals, not daily marathons.

PHASE 5: READY FOR INTERVIEWS (3 TO 6 MONTHS):

• A construction of web pages representing oneself will be the main platform for five to six different projects with their live demos, source links, and discussions about problems.

• Posted on LinkedIn about my progress, and contributed to open source.

PHASE 6: INTERVIEW EXPERIENCE(QUESTIONS):

ML Interviews

1. Most questions were about understanding and decision making, not math heavy theory.

2. Explain the bias variance tradeoff in simple terms

3. Why are neural networks usually not the first choice for tabular data?

4. How do you handle imbalanced datasets in real projects?

5. How would you evaluate and monitor a model in production, not just offline?

Coding Rounds:

1. Coding was not hardcore DSA.

2. Python data manipulation (Pandas, lists, dictionaries)

3. ML related logic problems

4. Focus on clarity and correctness, not LeetCode hard puzzles.

System Design:

1. These rounds tested how well you think end-to-end.

2. Design an AI recommendation system

3. Design a fraud detection system

4. Design a chatbot architecture (LLM + backend + data flow)

Key takeaway: Interviewers valued structured thinking and clear answers over "correct" ones.

Switching to AI is not about knowing everything. It is about building the right skills, thinking clearly, and showing real world impact through projects. This is just one path, not the only one. If you are consistent and focus on real projects, the transition is very doable especially if you already have software experience.

I have a degree in information technology, but I didn’t focus enough during my undergrad to really grasp technology as a whole. Now, I work in project management in the software space, but I don’t have a solid understanding of programming or the languages since I haven’t coded in a few years. I’m deeply curious about AI and tech’s future, purely for the sake of knowledge (not for a new job). I’m looking for a step-by-step roadmap, plus resources, to build a strong foundation in tech and AI fundamentals. I just want to understand how it all works, and I also want to know how to keep up with AI research and trends. Any advice on a roadmap or resources would be really appreciated!

I was hoping to learn full stack development with the resources on roadmap.sh however, I noticed for every node there are a couple of resources. Do I need to read all the listed resources or a few? How exactly should I tackle the resources I see? Also, for resources that point to the documentations, should I read the entire documentation or read only the page that is being linked? How exactly would you approach it in the most efficient and productive way?

I found roadmap.sh from someone online, I am wondering if this covers what I need to study and research to become one professionally. This would be under game-developer in the website.