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AI EngineerPutting an AI system into production, and keeping it there
The gap between a convincing demonstration and a system you let talk to your customers lies entirely in the constraints. That is the heart of this trade.
Why this role
A prototype is judged on what it gets right, a production system on what it cannot get wrong. The question is not whether the model answers well, but what happens the day it answers badly in front of a customer.
What it does concretely
- Define what the model is not allowed to produce, before what it should produce
- Take the binding data out of the model: closed list, server side calculation, review before display
- Set quotas, retention and conversation isolation from the day of deployment
- Read the real exchanges of the first days and keep enriching the source material
On a vehicle booking assistant, the vehicle comes from a closed list, the price is calculated in PHP from the real rate, and a filter replaces the whole answer if an amount appears where it should not.
What it is not
This is not prompt engineering. An instruction influences a model, it does not stop it: what stops it is code.
Two ways to access it
Inside your team
A profile joins your team for an agreed period, works on your tools and your data, and passes on what they build as they go.
On a deliverable
The role is mobilised on a project with a defined scope, alongside the other skills required. You receive a deliverable, not a report.
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See the role →Does this role match your need?
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