Staff Engineer, Software Distribution onPrem
Confirmed on the employer's own hiring board on Oct 25, 2026. First seen by Alion on Sep 25, 2026. Qevlar AI scores A on the Alion truth index.
About Qevlar AI
Qevlar is a B2B SaaS scale-up on a mission to boost the productivity of cybersecurity teams with Artificial Intelligence. We build an AI platform that transitions Security Operations Centers from reactive to proactive units through autonomous security alert investigations.
In just three years, we have:
Raised a total of $44m in series-A & seed rounds from EQT Ventures, Forgepoint Capital and Partech
Deployed our solution with major Enterprises such as Sodexo and GlobalConnect, and leading MSSPs like Orange Cyberdefense
Expanded to the US market with a growing North American presence
Ended 2025 with a strong multi-million dollar ARR and ambitious goals for 2026
We are launching Qevlar on-premise for customers who cannot let their security data leave their perimeter. Your job is to turn a signed bundle into a running production platform inside their datacentre, and to make each deployment faster than the last.
On these accounts the critical path is rarely the software. It is the security review board, the firewall change request, the PKI team, the change advisory board. Someone has to own that path, with enough technical credibility to be taken seriously by the customer's infrastructure architects: this is your role.
What You Will do
Decide what we do not port. Your first deliverable is the on-prem reference architecture and the explicit list of dependencies, services and features that will not cross the boundary. You will hold that line against the rest of engineering, with our backing.
Ship the bundle. A Helm chart and an air-gapped tarball that install on vanilla Kubernetes and on OpenShift, with no internet access, in under four hours - by someone who has never seen our code and has only your documentation.
Replace the cloud underneath us. Self-hosted Postgres, S3-compatible object storage, secret management, identity federation - behind abstractions clean enough that we never fork the product or let on-prem drift away from SaaS.
Package and size the inference layer. Our platform runs agentic investigations, which means sustained LLM throughput. You own the serving stack in the bundle, the VRAM and concurrency sizing matrix customers use to provision their own GPUs, and the distribution and rollback of model weights across an air gap.
Build the test harness that makes releases boring. Ephemeral customer-shaped environments, install and upgrade smoke tests across Kubernetes distributions, identity providers and network modes - until validating a release candidate takes a day, not a week.
Ship clean. Signed images, SBOMs, minimal base images, no critical or high CVEs at release. Our buyers are security teams; they will scan everything we hand them on day one.
Impose the upgrade contract on the whole org. Tested N-3 upgrade paths, backward-compatible migrations, no feature merged without a feature flag, a health check and a support-bundle contribution. Customers will jump from a year-old release straight to the latest, and it has to work.
Keep it boringly simple. A product that ships self-hosted cannot grow a new managed dependency every quarter. You get explicit veto power, and you will need it.
Who you are
You have:
Shipped software that someone else installs. This is what we screen hardest on. Operating your own cluster is a genuinely different discipline from handing an artifact to a stranger who runs it on infrastructure you will never see.
Deep Helm expertise - subcharts, conditional templating, values design - plus OCI registries and real exposure to Kubernetes beyond a single distribution: OpenShift and its SCCs, vanilla, RKE2, k0s, whatever you had to survive.
Worked in air-gapped or heavily restricted environments: image mirroring, offline bundles, egress proxies, custom CA bundles, no external DNS.
Replaced managed cloud services with self-hosted equivalents - Postgres operators such as CloudNativePG, MinIO or another S3-compatible store, Vault or External Secrets - and lived with the operational consequences.
Practical software supply chain security: image signing with cosign or equivalent, SBOM generation, distroless or minimal base images, CVE triage.
Strong infrastructure-as-code and CI (Terraform, Ansible, pipeline matrices). Here the test harness is the product.
Hands-on experience serving models on GPU - vLLM, NVIDIA NIM or TensorRT-LLM - including VRAM and concurrency sizing and the NVIDIA GPU Operator / Container Toolkit.
Strong Go and/or Python. You will change application code, not only YAML.
The judgement and the appetite to say no. Staff here means architectural authority and the willingness to use it.
Fluent in French and English.
Why join us
Work on meaningful problems with real-world impact in cybersecurity
A high-impact role in a small, international, fast-moving team where your contributions directly shape the company and its success
Shape the future of a product already making noise in AI + security circles
A company culture that values autonomy and creativity
Opportunity for equity and early ownership
We’re flexible on location - you can either work from home or join your colleagues in our beautiful office in Paris’ 9th arrondissement.
If you’re excited about helping security teams adopt cutting-edge AI, we’d love to hear from you. Let’s build the future of cybersecurity together!
Hiring process
Intro Call
Hiring Manager Interview
Technical interview
References and Offer