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Why I Joined an AI Bootcamp After Twelve Years as a Developer
Returning to a structured learning routine to keep growing as a hands-on engineer in the AI era
I signed up for the Codeit AI Engineer Sprint.
I have worked as a developer for twelve years. At one point, I worked as a game developer on a well-known 3D MMORPG, and after that I built and operated services at several companies. System design, meetings, code reviews, incidents, postmortems. None of that is new to me.
Still, I decided to become a bootcamp student again. I will follow the curriculum, watch lectures, and do assignments.
The Codeit AI Engineer Sprint starts on July 2. For now, the pre-course is open, and it includes three Python courses. It is preparation for AI engineering, but at this stage it feels more like warming up and reviewing the basics.
This was not a sudden decision.
My last career track was backend engineering, and for a while I had felt that I needed another step.
When a career gets long enough, people naturally start talking about management. Looking at the organization, managing people, and coordinating work are all important. I know that from experience. There was a time when I worked as a team lead too, but I eventually stepped back from it. It did not feel like the place I wanted to stand yet.
I still wanted to do the work itself better. I wanted to stay close to code, systems, and products that actually move.
Then AI started changing the ground under software engineering.
At first, it hallucinated a lot and did not feel reliable enough. It looked more like a coding assistant. Over time, the hallucinations became less frequent, and the tools became better at writing code, creating tests, and organizing documents. Then AI coding agents arrived, and after using them in real work, I could not treat them as simple helpers anymore.
In some moments, it felt as if AI had removed a large part of the act of coding from the software engineer’s job.
That does not mean coding disappears. But the center of the role is moving.
Typing code quickly may no longer be the main value. What matters more is whether I can tell if the AI’s direction is right, whether a design can survive real service constraints, and whether something that looks reasonable now might become a serious problem later. The work of an experienced engineer becomes more about review, direction, and judgment.
AI can produce code very quickly. That means the human side has to become sharper.
Around me, including myself, there was plenty of FOMO about falling behind AI.
But at the same time, I was interested.
If AI is going to change software this much, I do not want to remain outside it as someone who only knows how to use the tools. I want to understand how LLMs enter actual products, where AI engineering overlaps with backend engineering, where it separates, and what MLOps really deals with in practice.
Moving toward AI engineering or MLOps also looked like a good direction. It could become a career path. It could also become the foundation for another attempt at global big tech, where I had once stopped near the final door. And if the path changes later, graduate study is also possible. Something like Georgia Tech OMSCS, or a more research-oriented master’s program, could still be an option.
The reason was simple enough.
There is still a part of my knowledge I want to fill in. I want to keep growing. And I do not want AI to remain only a productivity tool that I use well from the outside.
Someone asked me this before.
“Why would a twelve-year developer take a bootcamp?”
It is a fair question. Especially when the course also supports people without a computer science background, it can look strange from the outside. Someone with years of production experience going back into a bootcamp can look like a detour.
For me, it feels closer to rebuilding a base before moving forward.
What I want to learn is not only Python syntax. I want to learn LLMs, AI engineering, and the practical sense of building products in this area from people who are already working deeply in it. I also expect to revisit things I thought I already knew, notice where my understanding was loose, learn the parts I do not know, and turn that work into projects and a portfolio.
That is enough reason for me.
There is also a personal reason.
Someone close to me recently joined a world-class big tech company. Originally, I had wanted to get there first, but that person got there before me. It is a little funny when I write it out, but it was a good push.
That person also supports the path I am taking now, and that made it easier for me to move forward.
The event changed the distance of the possibility. Something that had felt far away became part of the life of someone close to me. It made me want to prepare again. Working in a place like that together would be even better.
Of course, taking a bootcamp will not suddenly change everything. Global big tech, AI engineering, MLOps, graduate school. Any of those paths takes time. But I needed a starting point, and right now this course feels like one.
The pre-course I am taking now is made of three Python courses.
I have used Python when I needed it, but I have not used it deeply. I have written scripts, touched it around AI tools, and used it for work here and there, but I cannot say I learned it in a structured way. So it has been surprisingly fun to go through basic syntax and idioms one by one.
“Oh, so this is how they explain it.”
That small feeling is nice. When I revisit something I thought I already knew in a different order, it sometimes lands differently. Concepts I used to pass over quickly now connect to production experience in a different way.
That is why I wanted to write this first.
I will probably keep posting what I study during the Codeit course. Python will come first, then AI, LLMs, MLOps, and project notes may follow. Some posts will be close to course notes. Others will be about what the material looks like from the perspective of someone who has already worked in backend engineering for a while.
Before writing about what I learned, I wanted to write down why I started learning again. That way, the later posts will not feel like disconnected notes.
I do not know exactly how developers should change in the AI era. Things are moving too quickly, and today’s answer may become old in a few months. But standing still does not feel like the right option.
So I decided to move first and think again along the way.
The first course was Python.