As a Senior Site Reliability Engineer at our company 911, you'll own the infrastructure that keeps our platform reliable, scalable, and secure – work that directly supports mission-critical 911 systems used by public safety agencies. You'll drive infrastructure-as-code practices across AWS, lead observability efforts through Datadog, and bring modern AI-assisted engineering approaches into how the team builds and operates.
What You'll Do
Own and evolve AWS infrastructure using Infrastructure-as-Code (Terraform / Terragrunt)
Architect and scale AWS environments
Deploy, scale, and manage containerized workloads using Kubernetes and Docker; contribute to HA/DR architecture and platform strategy
Lead deployment and release processes using Argo (reference JD also names Bitbucket, Jenkins as part of the CI/CD toolset).
Define and enforce SLOs, SLIs, and error budgets; drive toil reduction across the platform
Drive full utilization of Datadog for monitoring, dashboards, and alerting across the platform (reference JD also names Prometheus, Grafana as potential observability tooling)
Build self-service internal developer platforms that empower teams to ship faster.
Take end-to-end ownership of infrastructure projects – define success criteria, execute, and measure outcomes.
Partner cross-functionally with engineering teams (e.g., network engineering, Dev owners) on long-term technical planning.
Bring AI-assisted engineering practices (e.g., Claude, MCP integrations) into daily workflows to improve team efficiency
Document work and provide cross-training to peers.
Resolve JIRA tickets across Cloud, CI/CD, deployments, and monitoring.
What You'll Do
Own and evolve AWS infrastructure using Infrastructure-as-Code (Terraform / Terragrunt)
Architect and scale AWS environments
Deploy, scale, and manage containerized workloads using Kubernetes and Docker; contribute to HA/DR architecture and platform strategy
Lead deployment and release processes using Argo (reference JD also names Bitbucket, Jenkins as part of the CI/CD toolset).
Define and enforce SLOs, SLIs, and error budgets; drive toil reduction across the platform
Drive full utilization of Datadog for monitoring, dashboards, and alerting across the platform (reference JD also names Prometheus, Grafana as potential observability tooling)
Build self-service internal developer platforms that empower teams to ship faster.
Take end-to-end ownership of infrastructure projects – define success criteria, execute, and measure outcomes.
Partner cross-functionally with engineering teams (e.g., network engineering, Dev owners) on long-term technical planning.
Bring AI-assisted engineering practices (e.g., Claude, MCP integrations) into daily workflows to improve team efficiency
Document work and provide cross-training to peers.
Resolve JIRA tickets across Cloud, CI/CD, deployments, and monitoring.
Requirements:
At least 6 years of experience as a DevOps/SRE engineer in a cloud environment
Hands-on, production-level AWS experience.
Hands-on production experience with Kubernetes and containerization
Experience with Terraform/Terragrunt (or similar Infrastructure-as-Code tools) – required
Strong Bash scripting skills
Deep understanding of SRE principles: SLOs, SLIs, error budgets, toil reduction, blameless post-mortems
Strong incident management / on-call experience
Solid understanding of APIs, microservices, and distributed systems
Demonstrated experience leading a project end-to-end, from defining success criteria through delivery and measurement
Communicates effectively across teams and can drive long-term technical planning
Practical experience with AI-assisted engineering tools (e.g., Claude, Cursor) and MCP-style integrations is a strong plus
Experience building AI/ML infrastructure (model deployment, inference pipelines)-plus.
At least 6 years of experience as a DevOps/SRE engineer in a cloud environment
Hands-on, production-level AWS experience.
Hands-on production experience with Kubernetes and containerization
Experience with Terraform/Terragrunt (or similar Infrastructure-as-Code tools) – required
Strong Bash scripting skills
Deep understanding of SRE principles: SLOs, SLIs, error budgets, toil reduction, blameless post-mortems
Strong incident management / on-call experience
Solid understanding of APIs, microservices, and distributed systems
Demonstrated experience leading a project end-to-end, from defining success criteria through delivery and measurement
Communicates effectively across teams and can drive long-term technical planning
Practical experience with AI-assisted engineering tools (e.g., Claude, Cursor) and MCP-style integrations is a strong plus
Experience building AI/ML infrastructure (model deployment, inference pipelines)-plus.
This position is open to all candidates.
