Should I Stick with Embedded Software Engineering or Transition to AI?

Hello everyone!

I graduated three years ago and have spent the past year and a half self-studying embedded software programming. Initially, I planned to pursue a career as an embedded software engineer. However, with the rise of AI, I’ve been questioning whether continuing down the embedded path is the right choice.

In my current role in the after-sales service department, I’ve been involved in AI project development, which I find incredibly interesting, but I feel I lack the foundational knowledge and skills in AI.

While I have worked on a smart lock project using STM32, MQTT server, and FreeRTOS, I’ve never applied Python to a real project, and my embedded learning hasn’t yet resulted in tangible, validated outcomes. I find myself stuck in a cycle of learning without seeing concrete results, and I’m concerned that switching to AI would only add to my uncertainty because I haven’t yet fully applied the skills I’ve learned in embedded systems.

I aspire to:

1. Extract data from databases like SQL or Snowflake and move it to a local data lake.

2. Process large datasets (millions of rows).

3. Leverage AI models, specifically large language models, to analyze long service ticket texts, summarizing key information like the issue (in no more than 5 words), the actions taken, and the solution.

4. Combine this AI output with the original data to create a web app that provides insights on common issues, most time-consuming problems, etc.

I’m wondering if anyone else has faced this dilemma: should I stick with my embedded software engineer path, or is it time to fully embrace AI? And if I do transition, what specific skills should I focus on, what learning path should I follow, and what projects should I pursue to gain hands-on experience in AI?

I’d love to hear your thoughts and any advice you might have. Thanks in advance!

5 comments