Staff Site Reliability Engineer
Lead EarnIn's AI-first reliability engineering strategy. Define SLOs/SLIs, build AI agents for incident response and on-call automation, and partner with engineering teams to embed AI-assisted operations across production systems on AWS.
About the job
Responsibilities
- Set a reliability strategy with AI at the center. Define SLIs, SLOs, and error budgets across critical services. Use AI to surface trends, predict capacity risks, and auto-generate reliability scorecards.
- Redesign the incident lifecycle around AI-assisted speed. Lead high-severity incident response as IC. Build AI-driven alert correlation and triage that reduces noise and accelerates root-cause identification.
- Drive adoption of AI-generated postmortems that surface systemic patterns and automatically track corrective actions.
- Build AI agents that draft runbook responses, pull relevant context from Datadog, incident.io, and Slack during pages, and recommend remediation steps.
- Partner with product engineering to embed AI-assisted investigation, alerting, and production readiness into their workflows.
- Guide service designs for graceful degradation, failure isolation, and capacity planning across EarnIn's AWS footprint (EKS, Kafka, DynamoDB, RDS, SQS).
- Use AI-driven analysis to identify architectural weak points before they become incidents.
- Coach engineers on reliability practices, run design and incident reviews, and build documentation and tooling.
Requirements
- 7+ years in SRE, Software Engineering, or Infrastructure Engineering with increasing scope and cross-org influence.
- Demonstrated experience applying AI/LLMs to operational workflows in production: alert triage/resolution, runbook automation, incident investigation, postmortem, or agentic operations tooling.
- Significant expertise with SLOs/SLIs, error budgets, incident command, and blameless postmortems in large-scale distributed systems.
- Meaningful software engineering ability (Python, Go, or similar).
- Deep observability experience (Datadog, CloudWatch, OpenTelemetry) with pragmatic, signal-heavy alerting designed for real human response, enhanced by AI-driven noise reduction.
- Solid infrastructure-as-code proficiency (Terraform, Kubernetes, AWS) with safe, reversible deployment practices.
- Proficiency with AI-assisted development tools (Cursor, Claude Code, Copilot) to accelerate engineering work and experience using AI-assisted development tools as part of software development workflow.
Nice-to-Haves
- Experience in fintech or regulated environments (SOC 2, PCI).
- Familiarity with FinOps or cost/performance tradeoffs in high-scale systems.
Skills
SRE, Python, Go, Terraform, Kubernetes, AWS, Datadog, CloudWatch, OpenTelemetry, EKS, Kafka, DynamoDB, Rds, SQS, Incident.Io
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