Is that what "AI Engineer" means nowadays? Is that what companies are looking for when they open recs for "AI Engineer"? Should I be marketing myself as an "AI Engineer" just because I'm very efficient using modern AI tooling to build good non-AI software?
When employers invent these titles like "AI engineer", they're looking for tech geeks who check off the keywords du jour. It's no different from the now defunct "blockchain engineer" of yesteryear. It's about broadcasting a particular skillset without really having anything to do with actual engineering. I guarantee you that the role of an AI engineer at one company will look very different in another – because it's not real.
If you did not get the above statement, people who changed their title to "AI Engineer" found it more easy to get jobs, at least from what I have seen.
- AI Engineer: an engineer who builds software that makes use of LLMs and other AI models, and maybe trains models (but not required)
- Agentic Engineer: an engineer who makes use of AI tools like coding agents when writing software.
AI Engineer was quite well established in the last few years to that first meaning, mainly thanks to swyx in 2023: https://www.latent.space/p/ai-engineer - which then lead to the popular AI Engineer Summit / World's Fair series of events https://www.ai.engineer/
But this year coding agents have become much more widely spread (the category didn't exist when AI Engineer was coined in 2023), so there's a possibility the term is being redefined to describe people who use those. I think that's a bad redefinition, personally.
("Agentic Engineer" is much less widely used, there may be other names for that category of engineer that I've not encountered yet.)
ML engineer: builds models and deploys them.
Hosted models have eaten a lot of the domain of ML but the difference is pretty clear in industries like recommendation, where LLMs are slower, less accurate, and cannot be personalized, not to mention orders of magnitude more expensive.
Agentic engineer would be someone who builds agents not just someone who uses them. Anyone can use Claude code.
I'll happily push back against that. Using Claude Code and similar tools effectively is way, way harder than people expect.
Anyone can pick up a guitar and strum the strings, but it takes a whole lot of work to actually get good with it.
No it's actually not that difficult. Even if you are not a "prompt engineer" you will get out what is needed from claude if you use simple logic (no need to be technical). In fact this whole "Prompt Engineering" scam will go extinct soon, as SOTA LLM's already catching up on most edge cases and prompting issues.
> Anyone can pick up a guitar and strum the strings, but it takes a whole lot of work to actually get good with it.
Really the wrong analogy here. You are comparing a human who learned guitar through years of practice. When with AI who itself already knows how to play. You're not learning an instrument. You're more like someone who just hired a virtuoso musician. Yes, you need to tell them what song you want,but you don't need to teach them chord progressions. The model already knows the hard part.
We need a name for engineers who don't use coding agents.
As for whether you should market yourself that way, I personally think your actual experience matters way more because most companies also haven’t hired many “AI engineers” before.
https://news.ycombinator.com/item?id=47975744
https://news.ycombinator.com/item?id=48099785
https://news.ycombinator.com/item?id=47978246
https://gavinray97.github.io/blog/absurdity-of-ai-engineer-t...
Integrating third-party libraries to build an application is a significant chunk of the work in any SaaS product and the expectation is you can read the vendor docs and figure it out
Nobody on earth can tell you that they've "mastered" the art of building software on top of LLMs.
They're weird. They don't behave like other APIs. They're non-deterministic and unpredictable and not even the people who created them fully understand what they can and cannot do.
(For one thing, if someone claims to have mastered LLMs ask them how they would 100% protect against prompt injection attacks...)
It's a speciality, just like being a payments engineer who integrates with systems like Stripe is a speciality.
Being familiar with agent-assisted development helps a little bit because at least you understand prompts, but there's a whole lot more to building software on top of LLMs than that.
Any engineer can get familiar with these things of course, just like any engineer can figure out what it takes to work on payment systems.
I had to migrate all of this from Braintree to Stripe. It probably encompasses the most complex payment system I've worked on in my career.
But that's not a job title, it's just part of "make the app work"
I don't think AI Engineer is an exclusive job title. If anything, coding agents are pushing us all to become generalists much more so than before.
I graduated from Dickmuth with honors.