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In 2022, after 14 years as a software engineer, I found myself reconsidering my career path. The fast-paced software world was no longer compatible with my preference for deep thinking and exploration. This post outlines my journey, ultimately leading me to choose IT infrastructure over AI roles, and how that decision aligned better with my interests and work style.

Disclosure: Some of the links below are affiliate links. This means that if you click through and make a purchase, I may earn a small commission at no extra cost to you.

1. Act I: Can Thinkers tinker?

Working as a software engineer can be stressful. The pace of modern/agile software teams is fast, bi-weekly sprints, continuous delivery, you build it … you run it, etc.. On top of that, having to learn the next programming tool/technique is very demanding.

I'm more of a thinker than a tinkerer; I like to look at topic from different angles, do some research, try a few experiments, step back and switch to something else while I let my brain process what I've learned, I might or might not come back to the topic depending on my level of interest or curiosity. My working preferences were not compatible with my programming job.

During my vacation around the end of 2021 I was going over my career prospects; I thought that I was ready to commit to a role long-term but I needed to do some research to understand my options and compare them somehow.

2. Act II: Educated Guessing

The first half of my career has been writing network clients and then writing application servers and release automation for the other half. I felt that I had been doing the same over and over only with different technologies and industry domains.

I didn't feel that I could make a drastic change like going out of the software scene since writing software was my main activity in the past 10 years. I am not a big risk taker so I only considered alternative paths that could benefit from my experience as a programmer.

I had narrowed down my options to two: software architecture and artificial intelligence.

In recent projects, my interest was often to get a glimpse of the high-level architecture and I would approach it by studying the delivery pipelines; when available, they describe the process each code change goes through to make it to a production environment.

Artificial Intelligence was interesting because it aligns well with personal interest in the automation of knowledge organization and discovery.

I found two resources that helped me Learn about what AI projects might look like in the industry:

  1. Chollet's book: Deep Learning with Python: the Universal Machine Learning workflow was exactly what I was looking for at the time, a high-level overview of the life cycle of an ML Project.
  2. Andrew Ng's content at deeplearning.ai: there I found descriptions of the different roles required to execute AI projects in the enterprise world. The site also has tools to asses your readiness for specific roles.

I was disappointed but not very surprised to learn that machine learning projects look very similar to other software projects.

AI/ML engineers bring their specialized skills to a project and were I to join an AI project, I would end up in a role almost indistinguishable to my current role, namely working on the delivery of AI-powered software to production environments.

3. Act III: non-committal commitment

During my vacation I made the resolution to double down and continue working as a software engineer and become even more proficient writing code for applications and release automation.

My resolution lasted about 4 hours after my return to work. On my first day after vacation, all it took was to read my mailbox and chat messages to remind me the many reasons why I was considering a change in the first place.

I started looking for a new role and that lead me into the world of IT infrastructure architecture. The company is large and for the past 50 years has deployed pretty much every piece of technology worth mentioning; my IT universe expanded and I felt quite good about not pursing AI/ML.

If you are considering a career in AI/ML, I highly recommend Chollet's book; it's a window into the world of machine learning engineering in the industry, directly from a very influential software engineer and AI researcher.

Alipay Code.