AI hiring plans built during the experimentation phase tend to centre on application and model-facing roles, because the priority at that stage is proving the technology can work at all.
Once AI moves into production, the parts of the stack underneath it- cloud infrastructure, platform engineering, security, governance and reliability- become the ones deciding whether the deployment holds up.
I’m seeing continued demand for engineers who can build secure, scalable environments underneath the AI-facing feature, not build the feature itself.
What to check in your AI hiring plan this week
Look at your current or planned AI-related team structure and check whether each of these is named explicitly, or assumed to be covered by the AI hire by default:
- Cloud infrastructure capability, so the system can scale without falling over under real demand
- Platform engineering capability, so the rest of the team can build on AI safely and repeatably instead of improvising
- Security and governance capability, so the AI system doesn’t become the easiest way into your data
- Reliability capability, so people trust the system enough to keep using it after the first failure
If any of these aren’t explicitly represented, that’s the gap to fix before the next hire goes out, well before the deployment runs into trouble.
Why a working pilot doesn’t guarantee a working deployment
A pilot only has to work once, in a controlled setting, for a demonstration.
Production has to work every day, under load, with real data, and it has to keep working when something goes wrong. That difference is why infrastructure and platform capability move from a background concern to a deciding factor as AI goes live.
A model can perform well in a proof of concept and still fail once it’s serving real users, because the environment underneath it was never built to carry that weight, even though the model itself hasn’t changed.
Build a dedicated hiring brief for this layer
If your current AI-related hiring plan is built mostly around application and model-facing roles, with infrastructure, platform, security and reliability treated as something the AI hire will presumably also cover, that plan is undersized for what deployment needs.
Cloud infrastructure, platform engineering, security, governance, and reliability all underpin successful AI adoption.
The demand I’m seeing is for engineers who can build those secure, scalable environments as a distinct capability set, not a side responsibility folded into a generalist AI role.
Ready to talk about your next engineering hire? Get in touch.
Liam
Technology & Software Engineering Recruitment
Last updated: September 2026
