Infra & DevOps Engineer

We are building an AI-native GTM intelligence platform for enterprise revenue teams. We hold 45M+ companies and hundreds of millions of contacts, and we run large data pipelines and AI inference on top of that.

Today we run on managed clouds (Linode, Azure, Scaleway). We are moving more workloads onto servers we own, because at our scale that is how we control cost. So this role needs someone who is strong at core DevOps and also comfortable running our own hardware, not only a cloud console.

We are looking for a solid DevOps engineer. We care most about two things.

First, the core craft. Linux, networking, Kubernetes, infrastructure as code, CI/CD, and observability, done well and in production. This is the foundation and it is a must.

Second, running our own servers. We operate bare-metal and dedicated machines, and we are putting more workloads there. You should be comfortable provisioning hosts, setting up networking and storage, and running Kubernetes on hardware we own, not only on a managed service.

We would rather hire one engineer who is genuinely strong at the core than a generalist who touches many things lightly. The pluses below are pluses, not hidden requirements.

• Infrastructure across our own servers and the cloud. Provision and run machines we own alongside our managed-cloud clusters. Own networking, storage, cluster lifecycle, and cost.
• Kubernetes in production. Keep our clusters stable and upgraded. Write and maintain the Helm charts and manifests. Debug real cluster problems.
• CI/CD that teams rely on. Fast feedback, safe rollouts, easy rollback, sane secret management. We use GitHub Actions today.
• Observability. Metrics, logs, and alerts that people other than you actually use. We use Prometheus, Grafana, and Loki.
• Cost. We have meaningful cloud credits and a growing bare-metal footprint. Making both go further through right-sizing, consolidation, and moving the right workloads to our own hardware is part of the job.

The foundation (must have)

• 4+ years in DevOps, infrastructure, SRE, or platform engineering.
• Strong Linux and networking. You can debug a host, not just restart it.
• Production Kubernetes, beyond bootstrap tutorials.
• Infrastructure as code (Terraform or similar) for real infrastructure, not click-ops.
• CI/CD pipelines that real teams depended on.
• Observability done right: dashboards and alerts someone else used.
• Good scripting (Python, Go, or Bash) for tooling and automation.
• You read unfamiliar code and debug without hand-holding.

What sets you apart (bare metal)

• You have run servers you own, not only managed cloud. For example: provisioning dedicated or bare-metal hosts, setting up the OS, networking and storage, and bootstrapping Kubernetes on them.
• You treat cost as a first-order concern and can explain where the money goes.

• GPU experience, even a little. Running GPU machines, drivers and CUDA, or serving inference (vLLM, TGI, Triton, or similar). We do more of this for AI inference and for processing data at scale, and you can grow into it here.
• Understanding of data pipelines. Enough Spark, Airflow, Iceberg, or Trino to size a job, read its execution model, and reason about resources. You would partner with the data team, not own their pipelines.
• Understanding of backend systems. Connection pooling, async, queues, and caching, and where runtime cost hides in the code.
• You use AI tools daily in your own work.

Our stack (context, not a checklist): Linux, Kubernetes, Terraform, Helm, GitHub Actions, Prometheus, Grafana, Loki, Linode, Azure, Scaleway, bare-metal and dedicated servers, and Spark, Airflow, Iceberg, and Trino on Kubernetes. You do not need all of it. We care more about how you think and what you can run reliably than which exact tools you have used.

• Real infrastructure problems: our own servers, multi-cloud, GPUs, and cost at scale.
• You help build the hardware and platform layer, not just maintain it.
• High ownership and direct impact on what we run and what it costs.
• A small, strong team and a fast pace.
• You report to and learn directly from the Chief Architect.