7+ years in infrastructure/platform/SRE roles with ownership of production systems.
Deep experience with Azure, particularly in high-security and regulated environments.
Practical AWS experience, especially with IoT services and event/stream-processing architectures.
Expert-level Kubernetes operations and strong Helm experience.
Strong GitOps background with a focus on improving delivery safety and speed.
Expertise in infrastructure-as-code using OpenTofu/Terraform.
Experience with observability platforms like Grafana and modern telemetry practices.
Responsibilities
Architect and manage secure, multi-environment infrastructure across AWS and Azure.
Develop GitOps pipelines and reusable Terraform-compatible modules with CI validation.
Operate and enhance Kubernetes platforms, focusing on reliability and automation.
Implement observability solutions using Grafana and modern telemetry.
Manage edge networking and IoT streaming pipelines for remote sensor data.
Support secure ML workloads and enhance platform security across various controls.
Benefits
Flexible work environment with a focus on work-life balance.
Opportunities for professional development and continuous learning.
Access to cutting-edge technology and tools.
Collaborative culture that encourages innovation and experimentation.
Full Job Description
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.
Cultural and Operating Principles
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.
Preferred Qualifications:
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.
Nice to Have
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.
Success Metrics
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.
