What are the responsibilities and job description for the Senior Infrastructure Engineer position at Quartermaster?
About Us
Quartermaster builds secure, mission-critical systems where reliability, observability, and security are non-negotiable. We are hiring a Senior Internal Infrastructure Engineer to help design and operate internal platform infrastructure across AWS and Azure. You will partner with engineering and security to improve GitOps delivery, infrastructure as code, Kubernetes operations, observability, edge networking, and support for IoT, streaming, and ML workloads in remote sensor systems.
What You Will Do
Faster lead time, lower change failure rate, and improved MTTR.
Secure, auditable infrastructure changes through GitOps and infrastructure as code.
Reliable operation of IoT, streaming, edge, remote sensor, and ML workloads.
Quartermaster builds secure, mission-critical systems where reliability, observability, and security are non-negotiable. We are hiring a Senior Internal Infrastructure Engineer to help design and operate internal platform infrastructure across AWS and Azure. You will partner with engineering and security to improve GitOps delivery, infrastructure as code, Kubernetes operations, observability, edge networking, and support for IoT, streaming, and ML workloads in remote sensor systems.
What You Will Do
- Architect and operate secure, multi-environment infrastructure across AWS, AWS Gov, Azure, and Azure Gov.
- Build GitOps pipelines and reusable OpenTofu (or similar) (Terraform-compatible) modules with strong CI validation.
- Operate Kubernetes platforms (including Helm) and improve reliability through SLOs, postmortems, and automation.
- Build observability with Grafana and modern telemetry (metrics, logs, traces, and alerting).
- Operate edge networking plus IoT and streaming pipelines (including AWS IoT and Kinesis) for remote sensor data.
- Support secure ML workloads and strengthen platform security across identity, secrets, policy, hardening, and compliance controls.
- Build systems that produce work: prioritize durable platforms and workflows over one-off heroics.
- Move fast: don't produce perfect solutions first, get on the board and then optimize
- AI-augmented execution: actively use and improve AI-assisted workflows for infrastructure design, operations, troubleshooting, and developer productivity.
- Safe agency: create clear guardrails, defaults, and feedback loops so engineers can move quickly with confidence.
- Empower engineering users: build self-service platform capabilities, paved roads, and documentation that reduce friction and accelerate delivery.
- 7 years in infrastructure/platform/SRE roles with direct ownership of production systems.
- Deep Azure experience, including high-security and regulated environments (Azure Government strongly preferred).
- Practical AWS experience, especially with IoT services, Kinesis, and event/stream-processing architectures.
- Expert-level Kubernetes operations experience and strong Helm experience.
- Strong GitOps background and a track record improving delivery safety and speed.
- Strong infrastructure-as-code expertise with OpenTofu/Terraform and policy-oriented workflows.
- Strong experience using observability platforms w/ Grafana (preferred) and modern telemetry practices.
- Experience with edge networking, distributed edge deployments, and remote sensor telemetry pipelines.
- Experience supporting ML/AI workloads and secure data processing at cloud and edge.
- Demonstrated security depth in IAM, network segmentation, zero trust, secrets management, encryption, and policy as code.
- Clear evidence of functionality-first thinking and comfort with AI-augmented engineering workflows.
- Experience with FedRAMP, NIST 800-53, CMMC, IL4/IL5, or similar frameworks.
- Experience with service meshes, policy engines (for example, OPA/Gatekeeper), workload identity, and software supply chain controls (for example, SBOM practices).
- Experience with multi-cluster or hybrid cloud/edge Kubernetes topologies.
Faster lead time, lower change failure rate, and improved MTTR.
Secure, auditable infrastructure changes through GitOps and infrastructure as code.
Reliable operation of IoT, streaming, edge, remote sensor, and ML workloads.