The AI/ML band didn’t shift — it split in two
Traditional ML sits where it was a year ago. The GenAI end has pulled away. Price them as one band and you lose the top while over-paying the bottom.
Draft. Figures marked like this are illustrative and pending verification against Recruise placement data & Sachith sign-off before publication.
Key takeaways
- The AI/ML band didn’t inflate uniformly — it bifurcated into two populations with different economics and different scarcity.
- Traditional ML engineering has held roughly steady, while the applied-GenAI end has pulled away on the back of thin supply.
- Price them as one band and you lose the top and over-pay the bottom — the worst of both errors in a single number.
One label, two labour markets.
“AI/ML” has become a band that describes two jobs pretending to be one. At one end sits established machine-learning engineering — feature pipelines, model training, MLOps — a deep, well-supplied talent pool whose price has behaved much as it did a year ago. At the other end sits applied generative work: people who have actually shipped retrieval systems, evaluation harnesses and agentic workflows into production, not just prototyped them. That pool is shallow, and it knows it.
The result is not a band that moved. It is a band that split. The midpoint you quote now sits in a valley between two populations, describing neither. That is why the number feels simultaneously too high for the candidates you can find and too low for the ones you want.
“When a band splits, the average becomes the one number no candidate recognises. You quote it, the traditional ML hire thinks you’re generous, and the GenAI hire has already stopped reading.”
Sachith Rai · MD & Founder, Recruise
The fix is to stop pricing the label and start pricing the proof.
The centres closing GenAI hires cleanly have quietly abandoned the single band. They ask a sharper question — has this person shipped generative systems into production, at scale, with the failure modes that teaches — and they price the answer, not the job title. The premium tracks demonstrated production experience, not years or credentials.
Do that and two things happen. You stop over-paying for traditional ML capability you can source at a fair market rate, and you free up the headroom to actually close the scarce end. Splitting the band is not a concession to inflation. It is how you spend the same budget more accurately.
One pattern worth knowing, every week.
The Signal is our weekly read on the senior GCC talent market — one chart, one pattern, no noise. Written from live placement data.
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