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 it Managed 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.
Identity and SSO fundamentals: OIDC and SAML, clients and credential rotation, redirect URIs, group-to-role mapping.
Linux, networking and TLS: DNS, routing, ingress and egress, certificate chains and interception proxies.
Kubernetes storage: persistent volumes and CSI, access modes, diagnosing shared storage that degrades under load.
A production deployment you personally carried and then supported. Name it, date it, tell us what went wrong.
Professional customer-facing English, and willingness to travel and work your customers' hours.
Also valuable: regulated or air-gapped deployments · private registries and image mirroring · GPU scheduling and LLM serving · enterprise IdP administration · Terraform and GitOps · Prometheus and Grafana · HPC schedulers · ISO 27001 evidence · Python · PLM, CAD or CAE exposure.
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