This is a remote position.

About the role :

A well-funded, seed-stage industrial-AI company is looking for an experienced, hands-on DevOps engineer who has already built and managed infrastructure in an early-stage startup or taken a system from zero.

You will own the cloud infrastructure and set the standards for security, monitoring and incident response.

You will also work as a core partner on the infrastructure that serves the company's main ML/AI models.

We need a versatile generalist who can work independently with little oversight and take on a wide range of responsibilities.

What you will own :

- Infrastructure : Design, deploy and run scalable, highly available infrastructure across multiple cloud environments using Infrastructure as Code (for example Terraform or CloudFormation).

- CI/CD : Build, run and improve the CI/CD pipelines used across the whole company.

- Observability : Set up and operate monitoring, logging and alerting (for example Prometheus, Grafana or the ELK stack) so services stay healthy and perform well.

- Security : Define and enforce security practice across infrastructure and applications, covering secrets management, network segmentation and vulnerability patching.

- Cloud cost : Look for savings in cloud spend on your own initiative and put them in place without hurting performance or reliability.

- Engineering support : Work alongside engineers to make development workflows smoother, resolve production issues and support their infrastructure needs.

What we are looking for :

- At least 5 years as a DevOps engineer, Site Reliability Engineer (SRE) or in a comparable role.

- Proven experience in a seed-stage startup or a ground-up build, and an understanding of what that means: limited resources, quick changes of direction and a hands-on, do-everything approach.

- Solid working knowledge of Docker and Kubernetes (EKS, GKE or AKS).

- Expert-level skill with Terraform or a comparable Infrastructure as Code tool.

- Experience building and maintaining CI/CD pipelines (for example GitLab CI, GitHub Actions or Jenkins).

- Hands-on database administration experience (for example PostgreSQL, MongoDB or Redis).

Nice to have :

- MLOps experience, including production serving infrastructure for machine learning models.

- Security certifications, or deep experience with cloud security frameworks.

- Experience with on-prem deployments and observability.

- Experience building and deploying ML models on real-time time-series data, such as sensor or IoT streams.

To apply :

Send your resume and a short example of infrastructure you built or ran yourself, with your role and the outcome.