Senior Software Engineer

AZX builds AI solutions for critical industries. The Senior Software Engineer will own the infrastructure for AI model serving and secure agent execution, including GPU workloads, Kubernetes systems, and sandboxing.

Responsibilities:

• Manage the serving tier for open-weight models: engine deployment and configuration, cold-start strategy, per-model SLOs, and upgrade/canary discipline
• Administer the Kubernetes layer for inference and sandbox workloads: operators and CRDs, autoscaling (KEDA/Karpenter-class), GPU scheduling and sharing, and node lifecycle
• Own the stateful data plane end to end with restore procedures that are regularly tested
• Oversee the sandbox runtime and its host-side control plane: lifecycle, exec, snapshot/fork, teardown, metering, and the threat model of the isolation boundary
• Direct the FastAPI control-plane services, Terraform/OpenTofu, Bicep, and the dashboards
• Run the layer the backend services team builds on, expect to debug into their services, and expect them to read your dashboards
• Manage the open-source posture: build to OSS standards and release as it matures, with reviewed PRs, real docs, and reproducible builds

Requirements:

• Applicants must be currently authorized to work in the United States on a full-time basis
• • 5+ years of shipping production systems in a systems language. Rust is the house language, but polyglots are welcome: deep Go, C/C++, or Zig with genuine appetite for Rust counts. Async runtimes, memory-safety discipline, and debugging at the syscall boundary should be familiar territory
• • Operated Kubernetes workloads that other people depended on: controllers or operators, scheduling, autoscaling, node lifecycle. You've been paged, and the experience changed how you build
• • Strong ability to threat-model isolation boundaries (namespaces, cgroups, seccomp, hypervisors), including identifying what an untrusted guest could observe, forge, or exhaust, and applying security best practices for agentic execution: least privilege, no credentials in the sandbox, audit trails, and human approval on write actions
• • Hands-on experience deploying or operating open-weight LLM serving infrastructure (vLLM/SGLang or similar), including packaging models into reliable, metered production endpoints
• • Performance discipline in distributed systems: you measure before you optimize, and you can tell the story of a latency you killed with the numbers attached
• • Practical depth in some of our core stack: Rust (tokio), Python/FastAPI, Kubernetes operators (controller-runtime/Kubebuilder/CRDs), KEDA, Karpenter, GPU device plugins/DRA, with a genuine willingness to research your way into the rest
• • Familiarity with isolation technology (Firecracker, Kata, gVisor, or comparable), secrets management and egress control (Vault/KMS-class), and hosting stateful systems (vector stores like pgvector/Qdrant, graph stores like Neo4j) with backup and failover discipline
• • Comfort operating across cloud and GPU substrates (AWS/Azure/GCP plus managed GPU clouds) using infrastructure-as-code (Terraform/OpenTofu, Bicep) and observability tooling (OpenTelemetry)
• • Bachelor's Degree
• Master's is a plus