Probably the only correct answer is to look at the job description under the title. Job titles in software engineering have always been flexible. What matters is what they actually want you to do. Even better is if you can figure out what problem they’re trying to solve because there can be better ways.
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.
- 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.)
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.
Considering 99% of engineers are using AI tools, that would mean all engineers are now "Agentic Engineers." Are we really no longer putting any value on someone who has expertise in understanding the code it produces?
I think "They wanted an engineer to build a chatbox that called ChatGPT with company documents as prompt context" fits the term "AI Engineer", personally - see https://www.latent.space/p/ai-engineer which uses it for "applied"
AI.
You don't call someone who integrates the Twilio API a "Twilio Engineer", or Mailchimp a "Mailchimp engineer"
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
I think the difference here is that it's possible to know bowering there is to know about the Twilio API. Read the docs, build a few things and you can consider yourself to have mastered that entirely.
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...)
Why would a self-described "AI Engineer" be any more capable of building that sort of functionality over any other backend engineer, especially one who is familiar with agent-assisted development?
Because building on top of LLMs is really tricky. You need to understand things like writing evals, configuring agentic loops, creating and iterating on system prompts, designing tools that work well with LLMs.
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.
> It's a speciality, just like being a payments engineer who integrates with systems like Stripe is a speciality.
At $PREV_JOB, we had physical Point-of-Sales systems as well as a mobile app, and provided multi-merchant marketplace functionality with things like disbursement reports and support for multiple bank accounts for vendors.
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"
Probably the only correct answer is to look at the job description under the title. Job titles in software engineering have always been flexible. What matters is what they actually want you to do. Even better is if you can figure out what problem they’re trying to solve because there can be better ways.
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.
I like these definitions:
- 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.)
AI engineer: makes API calls to a hosted LLM.
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.
Considering 99% of engineers are using AI tools, that would mean all engineers are now "Agentic Engineers." Are we really no longer putting any value on someone who has expertise in understanding the code it produces?
Right, that's why I don't think that class particularly needs a name. It's trending towards "software engineers" now.
We need a name for engineers who don't use coding agents.
I wrote an entire blogpost about this ridiculous phenomenon:
https://gavinray97.github.io/blog/absurdity-of-ai-engineer-t...
I think "They wanted an engineer to build a chatbox that called ChatGPT with company documents as prompt context" fits the term "AI Engineer", personally - see https://www.latent.space/p/ai-engineer which uses it for "applied" AI.
You don't call someone who integrates the Twilio API a "Twilio Engineer", or Mailchimp a "Mailchimp engineer"
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
I think the difference here is that it's possible to know bowering there is to know about the Twilio API. Read the docs, build a few things and you can consider yourself to have mastered that entirely.
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...)
Why would a self-described "AI Engineer" be any more capable of building that sort of functionality over any other backend engineer, especially one who is familiar with agent-assisted development?
Because building on top of LLMs is really tricky. You need to understand things like writing evals, configuring agentic loops, creating and iterating on system prompts, designing tools that work well with LLMs.
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"
At my $PREV_JOB we would have called you a payments engineer for that.
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.
Hi, I'm a AI Engineer at Poopshit dot com.
I graduated from Dickmuth with honors.
The definition is definitely changing.. or the way people are using it. AI PM used to mean something very different than what it does now as well!
Somebody soon to lose their job?
Soon to be a “facilities engineer” lol