AI Job Postings: Titles and Skills Have Come Apart

Nearly 50,000 postings, and half of AI roles blend two jobs

The analysis published by Andela on 17 September 2026 covers nearly 50,000 engineering role descriptions and reaches a blunt conclusion: the titles on AI job postings do a poor job of describing the skills required.

Across more than 1,800 open positions for AI engineers, ML engineers and adjacent roles, more than half blend skill sets belonging to at least two distinct jobs, as HR Dive reports^[1]. Among the items bundled together are LLM orchestration, autonomous agent architecture and vector database design.

The scope is precise: tech postings, collected by a talent tech company. The finding applies to anyone hiring AI engineering profiles, and stops there.

The method is textual analysis of published descriptions, with sample and date disclosed. There is one figure worth holding on to: more than half.

The "AI engineer" label has stopped describing a job

Andela's head of research, Cory Hymel, puts the point plainly: many organisations say they are looking for an AI engineer, and the term stays enormously broad. The skills being requested drift away from classic back-end profiles, and people end up in the wrong chairs.

The same analysis identifies new bundles, among them the LLM application engineer: the person who builds on top of foundation models rather than training them.

The tech sector invents roles faster than it can name them. Product managers, front-end and back-end developers, DevOps specialists: the descriptions overlap and the boundaries blur.

A title is a promise made to two parties. It promises the reader a recognisable job, and it promises the hiring side a standard of judgement. When the promise stays vague, both parties work in the dark.

The defect sits upstream of the hiring process

The prevailing reading blames these numbers on talent scarcity. The evidence says something else: a job architecture defect, sitting upstream of every interview.

When a position adds up two jobs, evaluation loses its reference point. The organisation compares people against a label, instead of a profile defined by real activities, degree of autonomy and expected outcomes.

A predictable chain follows: inconsistent screening criteria, interview panels weighing different things, offers built on an imagined market. The failure surfaces after the hire, when the actual work takes shape. And the cost gets filed away as a recruiting error.

The error, in fact, sits two steps earlier, in the document that defines the role. It is an organisational design decision, with an owner that in many structures remains ambiguous.

What happens to people assessed against a label

Anyone hired through an ambiguous posting arrives in a role that is still undefined. The first months are spent negotiating the boundaries of the job, instead of producing value.

The effect on evaluation is more serious. Vague criteria leave room for the implicit preferences of whoever decides, and judgement slides toward resemblance to the assessor. That is the ground where bias grows best: absent a clear scale, familiarity wins.

The issue also touches entry-level hiring. A posting that adds up two jobs raises the experience threshold required, and closes the first door to junior profiles who would quickly learn one of the two halves.

The first door closes because of a writing choice, before it closes because of a budget choice.

The market is already paying the price of ambiguity

The cost of this confusion shows up in advertised pay. The Indeed Hiring Lab analysis published on 17 September 2026 finds that AI exposure is pushing advertised pay in US job postings up rather than compressing it (Indeed Hiring Lab^[2]).

Two signals published on the same day converge. Titles multiply and lose definition; the pay promised goes up.

An organisation that gets the role definition wrong is therefore paying a premium on a profile it describes poorly. The wage premium tied to AI skills is by now an established fact in the labour market literature. What is new concerns where that spending lands.

A share of these budgets funds positions whose architecture remains undefined. It is a cost line the CFO sees as compensation, and that originates as design debt.

Who owns role architecture

The governance question is simple: who owns role definition when the work changes every quarter?

In most structures the answer stays split between engineering, HR and finance. The split produces postings written by approximation, with each function adding its own line and leaving the overall picture uncovered.

Cornerstone research shows a wide gap between leaders who see AI's impact and the people who feel ready to face it. Leadership readiness remains the bottleneck for adoption, ahead of the technical skills of those working on the systems every day.

A summary reported by HCAmag^[3] describes AI transformation as 10% technology and 90% people. Role architecture is the exact point where that 90% becomes an operational decision, with a name and a date.

What high-functioning organisations do differently

The structures holding up through this phase share three observable practices.

• They describe the activities before the title
• They refresh role architecture on a quarterly cadence
• They convert internal talent before going to market

The first practice inverts the usual order. The role starts from a list of real tasks and expected outcomes, and the name arrives at the end, as a consequence.

The second treats job architecture like the budget: a living document, with periodic review and a declared owner. Josh Bersin describes HR intelligence moving to enterprise scale, with systems that read skills and roles on a continuous basis (Josh Bersin^[4]).

The third exploits an advantage already in the building. People who know the systems, the data and the customers learn model orchestration in a few weeks, while the external market demands months of search and a price premium on an ambiguous title.

What changes for decision-makers

The evidence translates into four distinct conversations, one for each table.

• CEO: bring the quality of role definition to the board as an indicator of organisational readiness
• CHRO: rewrite AI job families starting from observed activities, with quarterly review
• CFO: measure how much of the AI wage premium funds positions with no stable profile
• Talent & Compensation Committee: track the internal conversion rate on open AI roles

Half of all postings blending two jobs is a board-table figure, on a par with a financial indicator. It says something about the organisation's ability to name its own work while that work changes.

The design question for next quarter is concrete: how many of the AI positions open today have a description that an internal colleague would read and recognise as a single job?