AI is scrambling the tech job market
- Andela analyzed roughly 50,000 AI job postings
- Over half of AI engineer roles conflate two-plus skill sets
- Andela found 1,800-plus unique AI/ML engineer job titles
- New titles like LLM application engineer lack certification tracks
NEW YORK, UNITED STATES — An analysis of roughly 50,000 job postings has found that more than half of artificial intelligence (AI) engineer roles conflate two or more distinct skill sets, creating systematic mismatches between the talent enterprises need and the workers they are actually hiring.
AI engineer roles conflate skill sets, leaving employers with mismatched hires
Cory Hymel, head of research at Andela, told HR Dive that companies are “saying, ‘I need to go hire an AI engineer,’ but that’s an extremely broad term,” with hiring managers searching for skill sets that “do not map to typical back-end engineering roles.”
He said employers are “putting the wrong butts in the wrong seats, because no one really knows what these underlying skill sets need to be and what to call them.”
Andela’s analysis of roughly 50,000 postings identified more than 1,800 unique AI/ML engineer job titles, with more than half of those listings conflating skill requirements from two or more distinct roles.
The mismatch spans software, data engineering, and machine learning domains, where single postings routinely require cross-functional expertise that no single career background reliably provides.
The classification failure reflects not a shortage of AI talent but a market-wide inability to define what AI engineering roles actually require.
New AI job titles emerge faster than talent pipelines can form
Hymel said “the pace of the technology and the skills required are maturing so quickly that employers are starting to accumulate talent debt in ways they’ve never had to deal with before, and that obviously then gets expressed into job change.”
Andela identified a category of “skill bleeds” where required competencies cross traditional role boundaries, producing postings for new positions including large language model (LLM) application engineer, DevSecOps security engineer, docs-as-code engineer, lakehouse analytics engineer, and FinOps reliability engineer.
These emerging titles carry no established educational or certification track, leaving hiring managers evaluating candidates against standards that do not yet exist.
The gap between job title and required skill set is producing systematic hiring mismatches that Andela’s data suggests will deepen as new AI role categories continue to emerge.
The fragmentation of AI engineering roles has direct implications for offshore outsourcing and business process outsourcing (BPO) providers that source and develop technology talent for enterprise clients.
When enterprise buyers face talent debt from poorly defined AI engineering hires, top BPO companies worldwide with structured competency frameworks for AI-adjacent roles are positioned to staff and develop those functions more precisely than open-market hiring allows.
Andela’s data confirms that the AI job market is fragmenting faster than employer classification systems can adapt, producing a sustained mismatch between posted roles and the talent required to fill them.
For enterprise buyers accumulating talent debt from mismatched AI engineering hires, outsourcing partners with established competency frameworks for AI-adjacent roles offer a more structured path to building capable teams.
BPO operators with defined talent pipelines across AI-adjacent functions, from data engineering to LLM application development, are positioned to convert employer classification failures into long-term managed service engagements.
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Disclosure: Outsource Accelerator uses AI tools in the backend of its editorial workflow. Every article is reviewed and verified by a human editor before publication.
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