47,000 job listings reveal the engineering roles that AI is creating
Every major transformation in tech has led to roles merging, then new ones emerging. Friction between developers and operations drove the creation of the DevOps engineer. Then, when security needed to be considered throughout the delivery pipeline, DevSecOps emerged.
The team beyond the AI-native talent and services platform Andela analyzed 47,000 recent engineering job postings from Fortune 500 companies. This research, released on Thursday, uncovered more than 2,000 skills that pour into 23 emerging job titles. None of these are coming out of nowhere; they strategically merge existing skill sets to create new roles.
Among 1,832 postings titled primarily for AI or ML engineers, 53% contained at least two skills drawn from different established roles, Andela finds.
In today’s tighter economy and amid AI, companies seem to be going one of three ways. They are lumping too much work and required experience into now-nebulous AI engineer or machine learning (ML) engineer job titles. They might be looking to replace tech workers with AI. But more forward-thinking organizations are reworking job titles and descriptions to reflect the demands of getting AI safely and efficiently through the software delivery lifecycle.
Cory Hymel, head of research at Andela, tells The New Stack, “If you’re going to look to deploy AI within your organization, the way to look at it is that an AI has a certain set of skills, and then a human has a certain set of skills.
“If you Venn diagram those and see where they cross over, an AI should do the skills it can. But the human circle is still exponentially larger than that of AI.”
“When you’re looking to deploy AI, it’s not about trying to replace that human circle with an AI one. It’s about what certain skills you need to carve out and delegate to it.”
Read on for the top engineering jobs that are emerging because of AI, how to attract tech talent for them, and what you need to focus on to get a tech job in this tough market.
AI is not serving the generalist. Specialization is still key.
Citing the leading AI CEOs, Hymel remarks, “You’ve heard from the AI salespeople of the world that AI is going to push people to be more generalist, and the data that we found here doesn’t necessarily support it.”
“You’ve heard from the AI salespeople of the world that AI is going to push people to be more generalist, and the data that we found here doesn’t necessarily support it.”
Overall, they found that these emerging job titles aren’t generalist at all. These emerging roles bridge skill sets from several existing ones, but each addresses a specific operational or product need, some tied to AI adoption.
The top five new engineering job roles discovered are:
- MLOps pipeline engineer, who builds and runs the automated infrastructure to deploy, version, and monitor machine-learning models in production, with 46% ML engineer, 23% DevOps engineer, 15% data engineer skills, and 8% each AI engineer and data scientist roles.
- LLM application engineer, who builds on and evaluates foundational models via large language model application and conversation systems, bringing 48% AI engineer and 34% ML engineer, with a touch of product designer, software architect, and embedded software engineer roles.
- FinOps reliability engineer runs cloud infrastructure for both reliability and cost, bridging 36% DevOps engineer, 27% site reliability engineer (SRE), 18% cloud engineer, and 9% each DevSecOps engineer and cloud solutions architect.
- Docs-as-Code engineer applies program management and DevOps engineering skills to the traditional technical writer’s role, pivoting from stagnant docs to specification-as-code.
- Product frontend engineer is about a third traditional frontend engineer and a third product manager, with a touch of full-stack engineer, UX researcher, and product designer.
“If you’re a DevOps engineer, historically, your skill bundle might have allocated 30 to 40% of pure DevOps-required skills that are rich and specific to that role, and you have a remaining bundle that is cross-role habitable, meaning that those skills would translate between DevOps or to an engineer or to a technical product manager,” Hymel explains. “Some of those skills can now be replaced with AI, which means that those skills that are more directly focused on your role become more important than ever.”
So-called “soft” business skills are also increasingly crucial, he contends. However, he seriously doubts anyone will ever be able to slide between finance, marketing, engineering, and sales roles.
Where enterprise engineering job descriptions falter
“Job descriptions and resumes right now are the best worst thing that we have. When you’re talking about large enterprises, and you’re having to deal with scale, your hiring process gets farther away from the work,” Hymel explains.
Especially when the hiring process starts in HR, not engineering, “you’re needing to put language in place that will survive the chain of custody, with the naming of the job [coming from] the engineer that’s closest to the work.”
It’s not uncommon for an enterprise to have 50 different front-end developer job listings, each with very different skill requirements. It’s better for candidates and for fit to be as specific as possible, including embracing new job titles.
This habit of generic job titles used to be positive because it brought in more applicants, but nowadays, with so many engineers on the market, it further dilutes your hiring pool, leaving you with the 100 fastest applicants—who are often AI-generated anyway.
“Any company that has not taken a hard look at revising their job postings and job titles is at an extreme disadvantage because there’s a very high probability that you’re going to end up hiring the wrong person simply because you didn’t take the time to describe the role well enough,” Hymel remarks, which leads to dire consequences.
“There’s potential churn, so you just spend all this time and cost to go headhunt and find someone. Two, if they do get in there, you have to pay for their ramp time to get up to speed because they were sold a different bill of goods than what was in the description. And then three, it impacts overall roadmaps and timelines because now you might have to replace, and, again, you have to wait for people to get up to speed.”
On top of this, HR and engineering hiring managers alike are using AI to generate job descriptions. It still isn’t recommended to have AI generate something so human and essential to your core success.
Especially in this time of flux, when no one may have the required experience, companies should start job descriptions with what they want the future hire to achieve.
“The cost of code is going nearer to zero.”
“The cost of code is going nearer to zero.” Hymel explains organizations should think more like, “Here are the outcomes that we’re looking for. If you have the soft skills and additional skills around it to get there, whether that is backlog prioritization, being able to be collaborative, having worked on project deployments before, and we don’t necessarily care that you can score a 10 out of 10 on Python anymore.”
Which emerging roles engineers should pursue
The familiar claim that women apply only when they meet every qualification is not well supported; recent research finds that application behavior is more complicated. Still, clearly separating essential qualifications from preferences can reduce ambiguity and unnecessary barriers.
Focusing on outcomes and clearly distinguishing required from preferred skills may broaden the applicant pool, although it does not guarantee greater diversity.
For example, if you’re an engineer who enjoys having a product focus, collaboration, and strategy, Hymel recommends looking toward the new product front-end engineer role, which owns the full user-facing feature lifecycle, from definition to shipping.
“You are required to have more mindshare towards prioritization of features,” he says, shifting away from a ticket person, because “now you have more control because AI allows you to span out a little bit deeper.”
Similarly, AI has the back-end engineer thinking beyond the back-end stack to deployments, scalability, and the reliability of underlying infrastructure systems, giving rise to roles like the polyglot back-end integration engineer.
Technical writers — reasonably worried about their jobs in the face of AI-generated documentation — should look toward new docs-as-code engineer positions, which add technical program management and DevOps engineering skills.
“If you’re writing the docs, you’re essentially writing the specs that enable spec-driven development. You now have the capability to actually contribute software,” Hymel observes. “And it starts all the way at the top too. If you’re a product manager, you can now start building and contributing code, like a product experience designer.”
Read the full Emergent Role Research. If any of these AI engineering job descriptions ring truer than what you were hired for, we hope it empowers your next conversation with HR or for you to apply for a different job title.
