Senior Site Reliability role arta
· full-time.
devopsroles summary, based on the employer's posting.
What you'll do- Build checks, staged releases and recovery paths so AI-assisted code changes reach production safely
- Set SLOs, SLIs and error budgets, then use them to balance delivery pace with system stability
- Create agent workflows for alert triage, incident investigation, maintenance and reporting, with verification and approval steps
- Improve logs, metrics, traces and alerts, and turn incident findings into runbooks, tests and safeguards
- Maintain Terraform and delivery tooling while improving GCP networking, access controls and capacity management
- At least six years in SRE, production engineering or infrastructure-focused backend work, with direct responsibility for live systems
- Strong hands-on GCP skills, including Cloud Run, networking and IAM; cloud experience is assessed in interviews
- You work fluently with Terraform, CI/CD and release safeguards, and can build operational tools in Go, Python or a comparable language
- You can lead incident investigations, find root causes and drive changes that prevent repeat failures
- You use coding agents in practice and can review, test and validate their output
This senior role suits an engineer who wants to make AI-assisted development faster without giving up verification, human judgment or production safeguards. You’ll collaborate asynchronously with a distributed team and need strong written English.
From the employerThe Role
Help Rabbit build and ship faster with AI — safely, securely and reliably.
Rabbit runs automated cost optimization across enterprise Google Cloud environments. When we change a customer's Big Query reservations or rightsize their GKE clusters, those changes need to be correct and dependable. Reliability is central to the trust customers place in our product.
Our foundation is already in place: logging, alerting, automated deployment and Terraform-managed infrastructure. Your mission is to evolve that foundation for an AI-accelerated engineering team: turn faster implementation into faster, dependable delivery through automated validation, safe releases and rapid feedback.
You'll apply proven SRE practices — SLOs, observability, incident response and deployment safety — to AI-assisted development and agent-driven workflows. The goal is to increase how quickly the team can deliver verified improvements, while controlling production risk and reducing manual operational work.
What You'll Do- Make AI-assisted delivery faster and safer. Build automated validation, progressive rollout and recovery mechanisms that let engineers and agents move quickly with clear checks before and after changes reach production.
- Make reliability measurable. Define and operationalize SLOs, SLIs and error budgets, and use them to guide practical decisions about delivery speed, stability and reliability work.
- Automate workflows with AI agents. Identify repetitive operational work and build reusable agent-driven workflows for alert triage, incident investigation, routine maintenance and reporting. Add verification and human approval where needed, and measure the reduction in manual effort.
- Improve observability and feedback. Evolve logging, metrics, tracing and alerting so failures are detected early and changes can be traced, investigated and verified.
- Turn incidents into lasting improvements. Improve runbooks, investigation and blameless postmortems, and translate recurring problems into tests, safeguards and automation.
- Keep infrastructure reproducible. Extend our Terraform and delivery tooling so environments remain consistent and changes stay reviewable as the platform grows.
- Improve GCP reliability and efficiency. Strengthen our cloud…
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