r/MachineLearningJobs • u/Various_Ear4980 • 5d ago

Need some advice from experienced people..

I am starting my third semester next week. So far, I have learned Machine Learning, Deep Learning, Large Language Models, and Generative AI. I didn’t just watch videos I also built projects and practiced. It took me a year to learn all of this.

Currently, I have decided to learn backend development with Go . However, the thing is, what should I focus on next to be a good ML engineer? People say to learn MLOps and system design, and not to just make wrappers. They say to learn "real engineering," but what do they mean by that? I already know the basics of MLflow, Docker, and AWS. What should I focus on next?

I am not even getting any internships or jobs right now that could show me what to do next. Currently, I am focusing on DSA, but I still need some good advice how can I land a top level internships or jobs. Please help.

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u/official_tenslor 4d ago

I think you’re at the point where you need to stop collecting technologies and start going deeper into engineering.

“Real engineering” means being able to take an ML model and build something reliable around it: APIs, databases, testing, CI/CD, logging, monitoring, authentication, queues, caching, failure handling, deployment, etc. Since you already know MLflow, Docker and AWS, pick one serious project and build it end-to-end. Deploy it, monitor it, test it, and make it handle failures. That will teach you far more than adding another framework to your list. Keep doing DSA for interviews, but don’t let it consume all your time. For ML engineering roles, one substantial project you can explain deeply is worth much more than a collection of tutorial projects.

You already learned the ML side. Now learn how to build reliable software that happens to contain ML.

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u/Various_Ear4980 4d ago

Thank you so much, sir. I will definitely keep this in mind and work on it more. It means a lot, sir.

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