Staff Site Reliability Engineer

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.