TLDR
AI Engineers are specialized technologist who sits at the intersection of software engineering, data science, and data engineering. They build, train, integrate, and monitor AI and machine learning systems that can operate, learn, and make decisions autonomously.
What is an AI Engineer?
AI Engineers are first and foremost software developers and data scientists. They need to hold expertise in programming in Python, as this is the language that AI models use. They also need to deeply understand data science and how we can utilize large data sets to train AI models to accomplish specific tasks based on the software being developed.
According to Microsoft 1 they are responsible for developing, programming, and training complext networks of algorithms that allow AI to function like a human brain. In other words, they are specialized software engineers who develop, deploy, and optimize artificial intelligence models to be used within programs autonomously.2
Simply put, if a software solution will have an AI component that does some function without needing consistent human input, an AI Engineer would build it.
How Do They Differ from Standard Software Engineers?
While all AI Engineers are software engineers, not all software engineers are engineering AI. They certainly share overlapping skills, but the AI engineer takes their expertise in coding and designing software solutions to the next level by applying the artificial intelligence solutions within the program.
Most software engineers are not also data scientists or data engineers, though they certainly can be. Like many areas within computer science, the lines can be a bit blurry. It truly does depend on the type of developer. A backend developer usually has experience with data engineering concepts and works with data I/O a lot. Frontend developers, on the other hand, do not typically work with the data, as they are more focused on UI/UX.
One area that standard backend developers and AI Engineers have in common is the need to utilize API calls. This tends to give backend developers a leg up when wanting to upskill and advance their career into this path.3
It should also be noted that AI Engineers tend to need a deep understanding of mathematics and data analysis in order to properly train AI models and build out systems for efficiently storing and receiving data (vector databases). Microsoft recommends a tool kit of calculus, linear algebra, statistics, and probability.
What About Vibe Coding?
This is a question I see a lot right now - or, perhaps you’d say a misunderstanding about the term AI Engineering.
Vibe Coding is a term that was coined in February 2025 by OpenAI founding team member Andrej Karpathy4. It describes a style of software development where one uses natural language to have a LLM or coding agent5 write code for you. The advent of these tools has completely transformed how quickly a non-technical person can spin up a simple application and has astronomically increased the amount of code being produced daily. Wikipedia estimates that over 40 million GitHub repos have been created since Karpathy coined the phrase.
Vibe coding is great for fast, low-friction prototypes and simple web applications. Claude Code, for instance, excels at building self-hosted web apps or self-contained HTML files that can be fully interactive and quite robust.
It’s not great, however, for complex production ready applicaitons. Currently the tools struggle with authorization workflows, error handling, security, and testing - though the tools are constantly improving. I personally use Claude Code and Anthropic releases updates nightly… sometimes multiple times a day.
Where agentic coding tools excel is in the hands of software engineers who know how to guide the agent properly and avoid common pitfalls that non-developers wouldn’t know to watch out for. This is particularly important for security practices, but even applies to following clean code principles.
I have found that AI Engineers are often being attached to a sort of professional vibe coding type of role, better known as agentic engineering. Essentially, this is an experienced software developer utilizing the AI coding tools available to them to scale how quickly they can produce or maintain codebases. It allows the human developer to steer the project without needing to do the tedious coding work up front.
So What?
So what does all this mean or what do you do with the information?
Well, if you research AI Engineer Jobs you’ll find that they often come with a very high salary at large organizations, often in the multiple six-figures realm. It can be very tempting to think you’ll just vibe code your way into a role like this, but I don’t think it’s that simple.
If you truly want to become an AI Engineer, in the formal sense, you need a solid computer science and software development foundation with an emphasis on data science and mathematics. It is possible that you could find a role that doesn’t need as robust of a background, but I wouldn’t count on it.
If you want to become an agentic engineer, a professional power user of coding agents, you likely still need a foundation in software development. I’d steer you towards backend or full-stack development to get the most out of the tools, but chances are in the future we will have a need for all types of agentic engineers, including frontend or UI/UX specialists.
The reality is that the agentic coding tools that exist today aren’t good enough to just give a natural language prompt and end up with a fully baked and secure product that has an excellent and unique design with an even better user experience. It’s possible with the right person steering the tool, but autonomously it won’t happen.
Learn computer science fundamentals, how to work in a terminal, version control with git, Python (in addition to any other languages you want to understand or that apply to your flavor of development, such as Javascript for frontend), and what good software design entails. You can find courses on these principles in multiple places online.6
[!Note] I am not affiliated with any of the software development programs listed below and don’t receive any commissions for recommending them.
I personally have experience with Boot.dev as well as Free Code Camp - both of which I’d highly recommend.
Footnotes
-
Codex or Claude Code, for example ↩
-
Free Options: Free Code Camp W3 Schools Paid: Boot.dev technically can be used for free, but worth the paid investment for a full backend developer path. Data Camp Code Academy ↩