DevOps Engineer - (m/f/d)

voize

1d•Hybrid

About The Position

At voize, we believe the greatest gift to frontline workers is time - time to care, connect, and be present. Today, that time is lost to admin work that pulls nurses away from what matters most: people. We’re changing that by building the AI companion that frees nurses from admin, giving them back up to 30% of time. We don’t replace humans - we support and amplify their impact. Today, 1,100+ care facilities trust voize, and over 75,000 nurses rely on our AI companion to ease their daily workload. Our mission is backed with a $50M Series A funding led by Balderton Capital, with support from HV Capital, Y Combinator and other leading VCs. As a fast-growing team, we combine first-in-class technology with meaningful social impact. As DevOps Engineer, you will build and own voize's platform and infrastructure — from cloud and edge systems to ML data pipelines; ensuring our systems are secure, reliable, and compliant. You will enable product and ML teams to ship state-of-the-art healthcare AI with confidence to hospitals, care facilities, and mobile devices across Europe.

Requirements

Strong experience operating Kubernetes in production (cluster operations, troubleshooting, networking, storage)
Experience with GitOps workflows
Experience working with monitoring and observability stacks
Security-minded — you have strong understanding of encryption, access controls, secrets management
Solid Linux systems administration skills, and experience with CI/CD pipeline design and maintenance.
You are comfortable with taking shifts in on-call rotations.

Responsibilities

Own and operate our Kubernetes clusters (AWS EKS production, bare-metal K3s for ML training, on-premises appliances running K3s) using GitOps (FluxCD) and Infrastructure as Code (CloudFormation, Ansible)
Manage the lifecycle of on-premises gateway appliances deployed at customer sites — VM image builds, TLS certificate automation, staged rollouts via GitOps, remote monitoring and troubleshooting
Build and maintain monitoring, alerting, and observability infrastructure (Prometheus, Grafana, Loki, Tempo, OpenTelemetry) across cloud and edge environments
Drive compliance automation — security hardening, access controls, audit logging, encrypted secrets management (SOPS/KMS), and evidence collection for C5, HIPAA, and HDS certifications
Support and scale ML training and data processing infrastructure — GPU cluster management, training job orchestration, and data pipeline reliability, working closely with the ML team to power state-of-the-art speech and language models
Incident response — detection, triage, resolution, and post-incident reviews for infrastructure issues.