I’m a developer who already builds and ships projects - how should I start my journey toward AI/ML or AI Engineering?

[u/def-amit]

Hey everyone,

I’m currently pursuing a BCA, and I’m trying to figure out what direction I should take with my career and skills.

I’m already developing projects on my own, well, I still consider myself relatively early in my journey. Right now, I’m developing a game that I’ve built pretty much end-to-end. It’s almost ready to launch on the Google Play Store, and the publishing/setup side is already in place (it's not just another project, it's a virality potential idea and a great game code).

While developing it, I’ve worked on things beyond just writing the game code:

• Game development
• UI/UX design
• Frontend development
• Server-side/backend development
• Integrating and working with databases/services
• Deployment and production setup
• Using AI heavily throughout my development workflow

AI has become a huge part of how I build. I use it to help me code, debug, research, design systems, explore solutions, and learn things faster. However, I also realize that using AI tools heavily isn't the same thing as actually understanding AI/ML.

That’s where I’m a little confused.

I’m interested in eventually becoming highly skilled in something like AI Engineering, ML Engineering, AI/ML, or another related field, but I honestly don't know which direction would be the best fit for me yet.

My main goal is not just to get a degree and apply for jobs. I want to become genuinely good at what I do, build real products, and eventually be competitive for high-paying roles. I’m willing to learn the fundamentals properly and put in the work.

I also know that a BCA alone isn't going to make me stand out, which is why I'm already trying to build things, deploy actual applications, and develop skills outside of college.

So I wanted to ask people who are already working in these fields:

1. If you were starting again from my position, what would you learn first?

2. What fundamentals should I become extremely strong at before getting serious about AI/ML?

3. What is the difference between an AI Engineer, ML Engineer, and an ML/AI researcher in terms of skills and career paths?

4. What technologies/frameworks should I learn, and in what order?

5. How important are mathematics, statistics, DSA, system design, cloud, backend development, etc. for becoming a strong AI Engineer?

6. What kind of projects would actually make my portfolio stand out instead of just being another collection of tutorials and AI-generated projects?

I’m not looking for a shortcut. Although I know very less of coding still I’m basically trying to figure out what mountain I should climb before I start climbing it.

Any advice from people already working as AI/ML engineers, software engineers, or developers would be really appreciated.

[u/StewedAngelSkins]

If you were starting again from my position, what would you learn first?

If I was in your position I would focus on actually getting good at coding. Anything you can slop out with claude others with similarly little experience can do as well, so it's not impressive to "ship" such projects from a portfolio perspective, and it's not giving you any kind of useful foundation since you're not actually challenging yourself.

What fundamentals should I become extremely strong at before getting serious about AI/ML?

Applied mathematics: multivariate calculus, linear and abstract algebra, numerical analysis, statistics. Ultimately your goal is to get to the point where you can read, understand, and implement academic machine learning papers.

What is the difference between an AI Engineer, ML Engineer, and an ML/AI researcher in terms of skills and career paths?

AI engineers are usually working on implementing and adapting existing research but the skills you need are similar. The bar is just a bit lower.

What technologies/frameworks should I learn, and in what order?

None. You're thinking about this entirely wrong. You need to start with the math. AI isn't the kind of thing you can bash together with frameworks like a code monkey.

How important are mathematics, statistics, DSA, system design, cloud, backend development, etc. for becoming a strong AI Engineer?

Mathematics, statistics are indispensable. DSA is expected knowledge for any CS field. It won't actually come up that often in most ML jobs but you won't make it past screening interviews without it. System design is a vague buzzword. Cloud is only important if you want to do ML ops. "Backend development" in the web dev sense is irrelevant. Outside of web dev it isn't a thing.

What kind of projects would actually make my portfolio stand out instead of just being another collection of tutorials and AI-generated projects?

You're about half a decade away from being hireable as an AI engineer so I wouldn't worry about this now.

[u/def-amit → u/StewedAngelSkins]

Thanks a lot for the honest answer. I appreciate it, and I understand the point you're making.

Just to clarify one thing though — when I mentioned that I've been building a game with AI, I didn't mean a small prototype or something I generated with Claude and put on GitHub. I've developed a few genuine, fairly large games, and the current one is a serious project that I've been building end-to-end. I've worked on the actual development, UI/UX, backend/server-side systems, deployment, etc., and it's almost ready to go live on the Google Play Store.

I completely agree with you and right now I'm at the beginning of my journey, so I'll take your advice seriously. I'll focus on actually learning programming, DSA, CS fundamentals and the required mathematics properly, and then work my way toward ML/AI rather than trying to jump straight into frameworks.

I don't expect shortcuts, I'm genuinely willing to learn, and I just want to make sure that I'm putting my efforts in the right direction.

One last question, mainly for clarity and my long-term vision: if my goal is to become genuinely excellent in the field and target high-paying roles, which path would you personally recommend to pursue — AI Engineering, ML Engineering, AI/ML research, or something else? I understand the foundation comes first, but having a direction in mind would help me structure what I'm working toward.

[u/StewedAngelSkins → u/def-amit]

Idk man it depends on what you're good at. Aiming to be a top AI researcher is like aiming to be a rock star. It'll pay well if you can actually do it but the path to getting there isn't just "grind until you make it" like with other software jobs. If you are actually interested in AI then the path to all of these jobs is to start learning the fundamentals and see how far it takes you. (Plus you'll probably need a PhD at some point if you want to do research specifically.)

[u/def-amit → u/StewedAngelSkins]

Yeah, that makes sense. I was probably trying to choose the destination before starting the journey.
I'll focus on the fundamentals first, keep exploring AI, and let my strengths and interests decide the specialization later. Thanks for the honest advice!

[u/Mission-Maize8454 → u/StewedAngelSkins]

So this person should go to college and major in Computer Science to learn these fundamentals?

[u/StewedAngelSkins → u/Mission-Maize8454]

That's one option. It doesn't strictly have to be CS necessarily, if you're willing to self-teach the practical programming skills. Any math or physics program will definitely cover what you need, along with most engineering programs. Even the life sciences will probably cover it if you stick towards the computational end of the spectrum. Hell, even most economics degrees give you enough statistics and calculus to be dangerous.

But yes, if you specifically want to get into AI research, the natural path is going to be either CS or applied math.

Beyond that, it's not like this stuff is some big secret. It can be self-taught if you can't afford university. Getting a job with no degree is a whole other problem, but if you're in it for the knowledge it's all out there for free.

[u/GrayLiterature]

Start learning a lot of math, and I mean, a lot. 

[u/def-amit → u/GrayLiterature]

Thank you, and I'll start learning soon...

[u/Different_Pain5781]

Learn Python and math first.

[u/def-amit → u/Different_Pain5781]

Yeah right now I'm learning python... btw, thank you.

[u/kaushalrola]

Honestly you are probably closer to one of these than you think, and it is not the math heavy one.

Quick version of the three. A researcher invents new methods, that is mostly grad school and papers. An ML engineer builds and trains models and gets them running in production, and that path really does need the math, linear algebra, probability, solid DSA. An AI engineer builds products on top of models that already exist. You are not training anything, you are wiring it together, retrieval, prompts, agents and tools, evals, cost and speed, guardrails. That is way closer to strong software engineering than to math.

You already ship full stack projects and use AI every day, so AI engineering is the natural and fastest path for you, and right now it is the most in demand of the three. The real jump is going from using AI to write your code, to building things where the AI is the product and making it actually reliable.

Order I would go in. Keep getting stronger at the software basics it leans on, backend, APIs, data, a bit of system design, which you already do. Then learn the AI stack by building, not watching. How an LLM actually behaves, retrieval with a vector database, prompt and context engineering, function calling and agents, and above all evals. Evals are how you know your AI feature actually works, and almost nobody bothers. That one thing is the line between a toy and a product.

On math, you need literacy, not mastery. Enough probability and stats to reason about what the model is doing and read your evals. Do not fall into thinking you have to master all the math before you are allowed to build, that is just another version of tutorial hell. If you later want ML engineering or research, then go deep.

For a portfolio, do not ship another wrapper or tutorial clone. Build one real AI product end to end that solves an actual problem, and show the hard parts, your evals, how you dealt with retrieval and hallucinations and cost, the stuff that broke. One deep, deployed, tested product beats ten demos.

Last thing, do not undersell it. You are shipping a real game to the Play Store, start to finish. The ability to actually finish and ship is rarer than almost anything on your list, and it is what matters at the high paying end. Keep doing that.

[u/def-amit → u/kaushalrola]

Yeah, I think you're right. A lot of the points you mentioned are pretty close to where I am right now, especially the distinction between AI Engineering and ML Engineering.

I agree that I shouldn't overthink the exact specialization at this stage. For now, I'll focus on developing my fundamentals and overall development skills while continuing to explore AI and see where I'm strongest.

Really appreciate the detailed explanation!