We're looking for a Software Engineer, Infrastructure to build the systems that power Capitol's platform — from cloud infrastructure and Kubernetes operations to CI/CD, observability, and internal tooling. You'll make a huge impact as part of a small team and have meaningful influence over how we scale and secure our infrastructure as we grow. This is a high-ownership role. You'll be solving real problems across multi-tenant and single tenant environments serving enterprise and government clients. Responsibilities • Design, implement, and maintain scalable, secure, and compliant cloud infrastructure using Infrastructure as Code. • Build and evolve CI/CD pipelines that improve developer velocity without sacrificing quality. • Operate and improve Kubernetes-based systems, ensuring reliability across development and production environments. • Drive observability — defining metrics, building dashboards, and making system health visible and actionable. • Participate in on-call rotations, incident response, and postmortems. • Contribute to compliance efforts (SOC 2 and beyond), translating controls into infrastructure reality. • Build internal tooling and automation that makes engineers more productive. • Collaborate proactively across teams, surfacing issues before they become blockers. You May Be a Good Fit If You • Have 7+ years building and operating production infrastructure. • Have led complex, cross-functional infrastructure projects end to end. • Take ownership — you drive things to completion without waiting to be asked. • Communicate clearly and proactively with both technical and non-technical stakeholders. • Are fluent in Python, Go, Rust, or similar. • Have deep knowledge of Kubernetes, Helm, OpenTelemetry, and at least one major cloud (GCP, Azure, or preferably AWS). • Are experienced with Infrastructure as Code (Terraform, Pulumi, or similar). • Have worked with security or compliance frameworks (SOC 2, FedRAMP, ISO 27001, or similar). • Have enterprise software experience — you understand the constraints, expectations, and deployment patterns that come with large organizational clients. • Have operated data or Ai infrastructure at scale — experience running and supporting systems like Ray, Spark, or Temporal in production, where reliability and throughput directly impact model training or inference workloads. Strong Candidates May Also Have • Experience in early-stage startups or hyper-growth environments. • Familiarity with multi-tenant SaaS architecture, data isolation, and GitOps patterns. • Government or regulated industry experience. • Contributions to open source infrastructure projects
