Raft Staff Engineer (DevOps) at Raft responsible for securing, deploying, and operating Kubernetes-based platform infrastructure. Support CI/CD, GitOps, observability, and secure software delivery practices.
Responsibilities
Raft builds mission-critical data, AI, and operational platforms that support time-sensitive decisions across multiple classification levels and deployment environments. Our systems move from cloud-hosted development environments into classified, on-premises, and edge deployments where reliability, security, and repeatability matter.
As an DevOps Engineer, you will help secure, deploy, and operate the platforms behind [R]DP, [R]AP, [R]AIMS, and Raft’s MLOps workflows. This is not a narrow infrastructure role. You will learn how the software works, how data moves through the platform, how Kubernetes workloads behave in production, and how security controls become part of everyday delivery rather than an after-the-fact checklist.
What makes this role different is the breadth of what you’ll be exposed to. CI/CD, GitOps, Kubernetes, Helm charts, observability, secure configuration, data pipelines, ML serving, and customer deployments all show up in the same workstream. You will work alongside experienced platform, software, data, and security engineers while taking ownership of well-scoped work. The right person is curious, disciplined, willing to troubleshoot deeply, and ready to grow into broader platform ownership over time.
Implement, maintain, and troubleshoot Kubernetes-based platform infrastructure under the guidance of senior engineers
Build and maintain CI/CD and GitOps workflows using tools such as GitLab CI, Argo CD, Flux, Helm, and repository-managed deployment configuration
Support secure software delivery practices including container hardening, vulnerability scanning, image provenance checks, secrets hygiene, and policy-driven deployment gates
Help operate cloud, on-premises, and disconnected deployments across AWS, Azure, data center, and tactical environments
Deploy and debug services, Helm charts, service mesh configuration, Kubernetes networking, storage, and runtime behavior
Support observability across Prometheus, Grafana, Fluent Bit, Loki, Kibana, OpenTelemetry, or similar tooling so issues can be detected and resolved quickly
Work with software and data engineers to understand service dependencies, data flows, and operational failure modes
Support data and MLOps workloads that may include Kafka, Flink, Pinot, KServe, Kubeflow, Ray, GPU-enabled workloads, and model-serving workflows
Write maintainable automation and small tools in Go, Java, Python, Bash, or similar languages to reduce manual effort and improve repeatability
