Lead Site Reliability Engineer

What you'll do

• Build production-grade software including automation, control loops, and self-healing tools to eliminate manual operations.
• Implement declarative, intent-based systems to continuously reconcile actual state with a trusted source of truth.
• Instrument and collect telemetry at scale to drive detection, diagnosis, and closed-loop remediation.
• Define SLIs and SLOs with stakeholders and operationalize them via actionable, impact-based alerting.
• Own services end-to-end for reliability, performance, security, and cost while leading on-call and major incident response.
• Use enterprise-authorized AI to accelerate incident triage, troubleshooting, and post-incident analysis while validating all outputs.
• Lead the adoption of AI-assisted reliability workflows across the SDLC to improve CI/CD quality and operational readiness.
• Conduct resiliency design reviews and break down complex problems into digestible work for other engineers.

What we're looking for

• Formal training or certification on site reliability engineering concepts and 5+ years of applied experience.
• Strong production software engineering experience in Python, Go, Java, C++, or Rust.
• Proven ownership of production systems at scale, including on-call responsibility, incident response, and postmortems.
• Demonstrated experience using enterprise-authorized AI capabilities to improve SRE workflows with strong validation habits.
• Ability to evaluate AI-assisted operational recommendations for correctness, risk, and security alignment.
• Demonstrated SLI/SLO/error-budget practice and the ability to drive reliability decisions with data.
• Deep observability skills using platforms like Grafana, Prometheus, Splunk, Datadog, or Dynatrace.
• Strong *nix fundamentals and infrastructure automation experience with Kubernetes, Terraform, and CI/CD.
• Networking depth or experience operating network-adjacent platforms (preferred).
• Experience across multiple infrastructure domains or programming languages (preferred).
• Demonstrated ongoing AI skill development in context/prompt engineering and agent orchestration (preferred).
• Prior experience in regulated or large-scale enterprise environments (preferred).
• Experience establishing engineering culture (preferred).