Neural Concept embeds AI into design and simulation workflows so engineering teams make continuous, data-driven decisions instead of waiting on sequential design–simulation–validation cycles.
None of that matters until the platform is running inside the customer's environment, on their infrastructure, under their rules.
That is this role.
You take an enterprise customer and make the platform real for them: architected, deployed, integrated, cleared by their security organisation, and stable enough that their engineers stop thinking.
You own that outcome end to end for named accounts, as their primary technical counterpart.
What you will do The deployment, from the first architecture conversation through go-live and into steady-state operation.
Its integration with the customer's identity, data and engineering systems, and the infrastructure and model serving layer beneath it.
The relationship with their IT, security and platform teams: assessments, architecture reviews, and the call when something breaks.
The diagnosis when it does break, including when the cause sits outside our own software.
The runbooks, post-mortems and playbooks that make the next deployment easier, and the feedback loop into Product.
Where you will do itManaged Kubernetes across the major clouds.
On-premises and fully air-gapped clusters with no route to the internet, GPU infrastructure, self-hosted model serving.
Customer HPC environments, schedulers, and shared storage under real load.
If installing software with no internet access, on hardware you do not control, reads as the interesting part of the job rather than the obstacle, this will suit you.
Who you are You will often be the only Neural Concept engineer in the room.
We are looking for someone with:6+ years deploying and operating enterprise software in production, two of them in environments you did not control.
Kubernetes at configuration depth: how chart values become rendered manifests, and why a workload will not start.