Can you clarify what you mean by "AI engineering?" There are two main paths right now - this is from my experience as a software engineer overall for a decade, a data engineer of some form for all but a year and a half of that, and a DE/MLE working on AI R&D teams for the last 5.5 years.
1. MLE/DE/MLOps - this is more like typical software engineering. You're responsible for building data platforms, tools, monitoring, and more around the model development lifecycle. This can include: data ingestion, data architecture, data transformation and storage, automating and productionizing various workflows like training, evaluation, and deployment, monitoring deployed models, data monitoring (and building monitoring), tooling like feature stores (and libraries for R&D teams to interact with them) or internal deep learning frameworks, etc. You'll basically work as a part (or an adjunct to) the research team that is testing new model architectures, different approaches towards some goal, etc. These are largely taken from my own experiences and projects I've built. Skills: software engineering, Python, knowledge of the model development lifecycle, data architecture/engineering, some knowledge about the frameworks used, cloud platforms, etc.
Designing ML Systems by Chip Huyen is a great overview of all of this kind of work.
2. Research. This is actually building models, implementing papers, very occasionally (especially in big companies) doing publishable research. This is more akin to academic work (my educational background is in hard science academia), and requires a lot of paper reading, experimentation, etc. It will require knowledge of your niche (I mostly work with CV teams, for instance), strong math fundamentals, and very often a PhD.
I can tell you how I, as a self-taught software engineer with a bio education got here. My first job was a generic enterprise desktop application development role, randomly joined a data engineering team shortly after that not even knowing what DE was, but knowing I liked it. We worked on a massive distributed ETL system. I then joined my first startup, it was also a DE role, but we were a small group in a larger research team where I got my initial exposure to ML workflows and especially moving them to the cloud. We did some simple model training, data management, and building products around the models we built while also supporting the research efforts of the larger team.
I then went to another startup, where I had the sole responsibility of our research infrastructure (largely based on the strength of my knowledge of AWS and Python). I was the sole engineer on a team of CV researchers, and did things like automate their entire evaluation workflow and move it to the cloud, worked on the internal deep learning framework, and built a team to evaluate the current AI development lifecycle and design a platform to harden and optimize the process. Covid put the kibosh on that. I moved to another, earlier startup, doing similar work but more foundational - almost everything was built from scratch.
1. MLE/DE/MLOps - this is more like typical software engineering. You're responsible for building data platforms, tools, monitoring, and more around the model development lifecycle. This can include: data ingestion, data architecture, data transformation and storage, automating and productionizing various workflows like training, evaluation, and deployment, monitoring deployed models, data monitoring (and building monitoring), tooling like feature stores (and libraries for R&D teams to interact with them) or internal deep learning frameworks, etc. You'll basically work as a part (or an adjunct to) the research team that is testing new model architectures, different approaches towards some goal, etc. These are largely taken from my own experiences and projects I've built. Skills: software engineering, Python, knowledge of the model development lifecycle, data architecture/engineering, some knowledge about the frameworks used, cloud platforms, etc. Designing ML Systems by Chip Huyen is a great overview of all of this kind of work.
2. Research. This is actually building models, implementing papers, very occasionally (especially in big companies) doing publishable research. This is more akin to academic work (my educational background is in hard science academia), and requires a lot of paper reading, experimentation, etc. It will require knowledge of your niche (I mostly work with CV teams, for instance), strong math fundamentals, and very often a PhD.
I can tell you how I, as a self-taught software engineer with a bio education got here. My first job was a generic enterprise desktop application development role, randomly joined a data engineering team shortly after that not even knowing what DE was, but knowing I liked it. We worked on a massive distributed ETL system. I then joined my first startup, it was also a DE role, but we were a small group in a larger research team where I got my initial exposure to ML workflows and especially moving them to the cloud. We did some simple model training, data management, and building products around the models we built while also supporting the research efforts of the larger team.
I then went to another startup, where I had the sole responsibility of our research infrastructure (largely based on the strength of my knowledge of AWS and Python). I was the sole engineer on a team of CV researchers, and did things like automate their entire evaluation workflow and move it to the cloud, worked on the internal deep learning framework, and built a team to evaluate the current AI development lifecycle and design a platform to harden and optimize the process. Covid put the kibosh on that. I moved to another, earlier startup, doing similar work but more foundational - almost everything was built from scratch.