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Qevlar AI is a B2B SaaS cybersecurity scale-up that uses AI to help Security Operations Centers transition from reactive to proactive threat detection. The company has raised $44M from top-tier VCs (EQT Ventures, Forgepoint Capital, Partech), deployed with major enterprises like Sodexo and GlobalConnect, and ended 2025 with multi-million dollar ARR. They are now launching an on-premises offering for customers with strict data residency requirements.
In this Staff Engineer role, you will own the end-to-end deployment of Qevlar's on-premises platform into large enterprise datacenters. You will manage multiple concurrent deployments, each on its own customer timeline, from technical kickoff through production sign-off. Your responsibilities include:
**Core Responsibilities:**
- Run on-prem deployments end-to-end, with the first production deployment landing within your first few months
- Own the pre-flight phase: build and execute prerequisites assessments covering Kubernetes distribution, GPU capacity, storage, identity providers, network egress, and certificate authorities
- Navigate complex customer organizations: manage security reviews, firewall change requests, PKI coordination, and change advisory board (CAB) cycles to keep deployments on schedule despite not controlling customer resources or decisions
- Treat documentation as a core product: create install guides, prerequisites matrices, and troubleshooting runbooks that enable customer engineers to complete deployments independently
- Own the release train: manage version announcements, upgrade campaigns, and migration planning across a fleet where each customer runs a different release
- Run L3 triage: qualify issues, reproduce them from support bundles, and escalate to engineering with complete context about customer environments you cannot directly access
- Close the loop: convert field friction points into documented requirements, product tickets, and bundle improvements
- Measure and reduce cycle time: track deployment velocity from signature to production across your first four deployments and drive measurable improvements each quarter
**Requirements:**
- Proven experience deploying complex software into large enterprise datacenters as a deployment architect, delivery lead, professional services engineer, or technical program manager
- Deep technical foundation: ability to read Kubernetes values.yaml files, debug TLS handshakes, explain proxy behavior with custom CAs, understand OpenShift SCCs, and diagnose why features break without writing the feature itself
- Demonstrated ability to run multiple technical projects in parallel, each with distinct stakeholders, timelines, and success criteria
- Exceptional technical writing skills; documentation must be executable by strangers without requiring support calls
- Composure and credibility in conversations with infrastructure architects, security officers, and CISOs—these discussions are technical, adversarial by design, and account-critical
- Fluent in both English and French
- Experience navigating enterprise security review processes, PKI workflows, and multi-week change request cycles
