Once we were developers, software engineers, and data scientists; but no more! Today we’re all AI Engineers, right? Right!?! Swyx’s recent AI Engineer World’s Fair conference surfaced a lot of enthusiasm around the term by drawing 3,000+ founders and engineers to San Francisco. Microsoft offers an “AI Engineer using Microsoft Azure” course through Udacity. But the hype is especially pronounced on social media. In a Reddit post entitled “Is everyone an AI engineer now,” one Redditor quipped, there’s “lots of LinkedIn hype chasing happening” around this title. I have also personally encountered self-identified AI engineers both on LinkedIn and IRL (I met several last month at RenderATL).
What confuses me about this title is whether it is intended to suggest that AI Engineers have adopted AI tools as part of their engineering work, or if they work at a company with an AI product and/or are part of a team creating an AI tool. I am not alone in my confusion. Some in the developer community, like this Hacker News user, argue “I would never call myself an “AI Engineer” unless I was actually building custom LLMs.” Others, like this Redditor, disagree: “Does someone need to make React from scratch to call themselves a front end engineer, or is using React to make the front end make them a front end engineer?”
I made a chart to help with the work of disambiguation, and welcome others to keep it on hand in case they find themselves similarly befuddled. For the present, the term remains ambiguous enough to warrant asking where AI Engineers land within this quadrant of possibilities.
But perhaps this confusion will be short lived. Thought leaders have taken a stab at a definition. Jack Arenas, Principal at Founder Collective, for instance argues that:
What most companies call “AI engineering” today is really just good software engineering with some new tools and a few API calls.
The AI Engineer mostly rebrands old skills with new buzz. No self-described AI Engineers that I know are building fundamental models (which happens at places like Anthropic, OpenAI, and Google), but they may very well be focused on things from prompt engineering and fine-tuning, to building vector database pipelines, to coordinating fleets of AI agents. What is more, they may even be doing this as part of a startup building an AI product. It’s spheres within spheres, friends (and time to pull out my handy chart!).
Detractors joke that tech bros dilute the word “engineering” by slapping it behind the newest new shiny (NFT, Metaverse, AI). From that perspective, adding “AI” onto your job title might be perceived as cringey. But the AI Engineer strikes me as different. Regardless of the hype, something real is emerging. Taken at face value, an AI Engineer bridges the gap between traditional software and machine learning—not by conducting pure research, but rather through integrating AI into apps in a way that software developers historically haven’t had to before. The field has grown from a niche into a broad and evolving discipline. So, hype or not, the AI Engineer trend is riding on genuine technological shifts. According to “The State of AI Engineering (2025)” by Charlie Guo, Staff AI Engineer at Pulley:
As we enter an era where software development might be measured in compute budgets rather than headcount, where agent fleets tackle problems no single engineer could handle, and where AI might genuinely discover new knowledge, one thing is clear: the boundaries of AI engineering are expanding faster than any of us can fully grasp.
What started as a niche between ML and software engineering has exploded into a constellation of specialties. Voice engineers, AI PMs, eval designers, AI architects – job titles that didn’t exist last year are now entire career paths. The conference’s growth from 2,000 to 3,000 attendees understates the real expansion: the surface area of interesting problems has grown exponentially.
Strategic Advantage: Why Add “AI” to Your Title?
If you suspect people call themselves AI Engineers partly to seize the zeitgeist, you’re absolutely right. Developers are keenly aware that hot keywords catch recruiters’ eyes. One candid Redditor admitted:
I added ai engineer to my cv and LinkedIn, and that got me a paying client for the past 4 months and counting. On their payroll software I’m marked as “AI expert”. If it gets the bills paid plus loads of actual experience, why not?
The tech job market is brutal, so branding yourself with this label can be a smart move. Riding the AI wave on your resume can signal to employers that you’re up-to-date with the latest tech—even if all you did was a LangChain tutorial.
And it’s not just job seekers. Companies are leaning into the term too. Job postings for “AI Engineers” are multiplying as businesses scramble to attract talent with AI skills. Armand Ruiz, VP of AI Platform at IBM, has gone all in, explaining in his newsletter:
This role has emerged and is rapidly gaining recognition among startups and large corporations. At IBM, we are going all in with the AI Engineering profession. I hired in my team at IBM more than 300+ AI Engineers worldwide last year, from new graduates to very senior engineers and managers.
Employers see strategic value in the phrase: it promises that a candidate can inject AI into products, which sounds enticing when, as Thomas Ptacek, Associate Retail Sales Manager at Fly, puts it: “Tech execs are mandating LLM adoption.” In fact, Ruiz believes the AI Engineer will be “The highest-demand engineering job of the decade.” For startups and tech giants alike, having AI Engineers on the team (or at least on the org chart) signals to investors and customers that they’re part of the AI revolution.
Of course, rebranding roles can veer into buzzword bingo. Recall when every company in 2018 suddenly needed “blockchain developers,” when “Web3 Engineer” became a thing during the crypto hype, or “Big Data Engineers” emerged in 2022. I come by my skepticism honestly. I was hired as a frontend developer, and fondly recall when my company rebranded the “developers” to “engineers.” Our Slack lit up with images and gifs of train engineers in striped overalls and old timey hats. Same job: fancier title. I’m sure our clients were suitably impressed.
The AI Engineer craze takes this same impulse a step further. The danger is that because it is early days the term AI Engineer might not be the one that sticks. The London-based startup Tessl, for instance, is pushing the idea of an AI Native Developer. Moreover, companies may list AI Engineer roles without fully understanding what they need purely in order to avoid missing out. The result? Possibly inflated titles and salary bids for folks whose actual work might not differ much from a regular developer’s. No static from me! If calling yourself an AI Engineer gets you in the interview room, godspeed.
From Crutch to Superpower
An interesting wrinkle in the rise of the AI Engineer is that not so very long ago (circa 2023), there was a sense that using AI tools was “cheating” or a sign of incompetence. AI is a crutch: if you lean on it too much, maybe you’re not a “real” engineer.
Fast-forward to 2025, and this stigma is fading fast. Democratization of the technology is at the forefront of how Chip Huyen, author of the O’Reilly AI Engineering: Building Applications with Foundation Models, is thinking about the term:
Recent breakthroughs in AI have not only increased demand for AI products, they’ve also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications.
This democratization has flipped the script. As one Redditor gleefully observes:
I love it. Finally all the snobby ML experts are overwhelmed and they cant gatekeep outsiders anymore. End of “It’s an art, can not be explained” stock [sic] overflow answers and real innovation is happening because of this shift. New blood in the field is good and its mostly started because of standartizing [sic] interface to a tool.
The specialized academic knowledge once needed (PhDs in deep learning, etc.) matters a bit less when you can call an OpenAI API in 5 lines of code. Now the skill is less about inventing algorithms and more about applying them cleverly. Yes, AI can be a crutch if abused, but it can also be a force multiplier if you know what you’re doing. Engineering leadership in 2025 is coming around to this view. Despite concerning studies, like METR’s randomized control trial of open source developer productivity that found AI was not an accelerant, for many organizations not having AI skills is a liability. What’s clear is that using AI is rapidly becoming part of the expected skillset for all practitioners. Just as nobody would fault an engineer for using Stack Overflow or a compiler, few in leadership positions bat an eye now if their engineers use AI code assistants to accelerate their work. In 2023 you might have been teased for it; in 2025, you’re behind the curve if you don’t use it.
AI Engineer vs. Vibe Coder
In July 2025, the greatest threat to the AI Engineer’s dominance is the “vibe coder.” Andrej Karpathy’s super chill “vibe coding,” which he has already sought to replace with “context engineering,” has a decidedly different feel. Needless to say, I haven’t seen “vibe coder” on nearly as many LinkedIn bylines. So, what’s the difference between a self-proclaimed AI Engineer and vibe coder?
The AI Engineer, in theory, takes a more disciplined approach than Karpathy’s tongue-in-cheek way of describing coding at the nexus of intuition and natural language programming. Instead of carefully planning architecture, the vibe coder just says, “Hey AI, build me X with Y tech,” and iterates on whatever it spits out. AI Engineers resemble orchestra conductors by ensuring each AI component, API, and model works in harmony to solve the problem. It’s a bit less hip than vibe coding (lots of glue code, debugging, and prompt tweaking), and far more structured. I like this Hacker News user’s explanation of what this sort of orchestration looks like:
The “AI Software Engineers” at my company, whose applications and workflows I often support as an SRE, by and large build things with LLMs rather than train or create them.
What they build ranges from product features to internal tools. They make heavy use of LLM vendor inference APIs, vector databases, etc. They end up writing a lot of glue code and software to manage the context of the LLM, query for data, integrate with other systems and so on. They also develop front-end interfaces for their applications.
Only recently have they started to, lightly, explore the idea of training LLMs with managed services like AWS SageMaker.
All this is to say that, the “AI Engineer/SWE” title will probably represent vastly different things depending on the technical sophistication of the organization.
If someone told me they were an AI Engineer at OpenAI I’d be more inclined to expect their role to be more fundamental, elsewhere, not so much.
That may not sound sexy, but it’s what makes an AI product actually work reliably. They treat AI not as a mystical oracle they interact with using vibes, but rather as a component that needs engineering around it. That’s not to say vibe coding is bad. Speaking for myself, I am enjoying a “hot vibe code summer,” but for folks that want to transition their vibes into a more professional setting, vibe coding might be a stepping stone to AI Engineering. The more serious the project, the more users will need to rein in their informality. While the practitioners might jest that “Bro got a PHD in Vibe Coding,” the AI Engineer is actually doing the serious work of setting up experiments, monitoring outputs, and making sure the AI reliably does what it’s supposed to.
Final Thoughts
Five years from now will the AI Engineer become as standard as “web developer,” or will it become an archaism? There are arguments on both sides. On one hand, the momentum behind AI is enormous and sustained but unlike some past fads, AI is driving fundamental changes across software development. It’s also not confined to a niche. AI features are cropping up in every horizontal, including productivity tools, customer service, healthcare, finance, you name it. Moreover, top universities including my alma mater Carnegie Mellon are fundamentally rethinking the CS major and wondering “How Do You Teach Computer Science in the A.I. Era?” This all suggests to me that “AI Engineer” could actually stick as a meaningful long-term role. Moreover, thought leaders like Guo note that we’re seeing an explosion of sub-roles: “Voice engineers, AI PMs, eval designers, AI architects – job titles that didn’t exist last year are now entire career paths.” All of this seems to suggest that AI Engineering isn’t a single gimmick: it’s an umbrella for a new generation of AI-fluent specialists.
The term “AI Engineer” may be buzzwordy, but it’s also a sign of the times. It encapsulates the excitement (and anxiety) of a tech industry in flux—a world in which code and AI intermingle, and where knowing how to leverage AI effectively is no longer optional. For tech enthusiasts and engineering leaders, the rise of this title is both a caution and an opportunity. The caution is not to hire or self-label based on hype alone. The opportunity is to embrace the genuinely new capabilities that today’s AI Engineers bring. An AI Engineer worth their salt isn’t just prompting ChatGPT willy-nilly; they’re combining software craftsmanship with AI savvy. They know when AI is the right tool for the job and when it isn’t. They design with both user experience (UX) and AI experience (AX) in mind. In the end, whether the title catches on long-term doesn’t matter as much as the skills and mindset it represents.
Disclaimer: Microsoft, Google, Tessl, and IBM are RedMonk clients.