Staff Site Reliability Engineer

Ethos is hiring a Staff Site Reliability Engineer to set technical direction for reliability, infrastructure, and platform tooling. The role designs and operates production infrastructure, improves reliability and cost efficiency, and builds governed tooling for AI initiatives.

Responsibilities:

• Set Direction: Define and drive the multi-quarter technical strategy for reliability, infrastructure, and platform tooling, in partnership with SRE and engineering leadership and aligned with company OKRs
• Architecture: Lead the design of complex, cross-cutting infrastructure systems. Author and review technical specifications and architecture proposals to ensure consistency, resilience, and long-term maintainability
• Influence Without Authority: Motivate product engineering teams to adopt reliability best practices, and act as a trusted advisor on production readiness when new services are designed and brought online
• Mentorship: Grow the technical skills of SREs across our US and Bangalore teams through design reviews, pairing, and code review, and help raise the hiring bar by participating in interviews
• Service Standards: Establish the framework for how teams define, measure, and act on SLIs, SLOs, and error budgets. Help teams instrument and monitor them
• Incident Excellence: Serve as a senior technical leader during high-severity incidents. Drive a culture of rigorous, blameless post-mortems and ensure follow-up work actually reduces recurrence
• Resilience: Lead capacity planning, disaster recovery, and failure-mode testing so our systems degrade gracefully and recover quickly
• Observability: Evolve our monitoring and alerting strategy so failures are detected before they impact customers and on-call is sustainable
• Scalable Systems: Design, build, and operate our AWS cloud infrastructure using Infrastructure as Code (Terraform), Kubernetes, and GitOps workflows (ArgoCD, Atlantis)
• AI Gateway & Governance: Architect and build governed gateways for AI model routing, including model access controls, audit trails, and secure data connectors (such as MCP) that prevent sensitive data exposure
• Security & Compliance: Ensure infrastructure, including AI prototyping environments, meets security and regulatory requirements through automated scanning, PII guardrails, and scoped data access
• FinOps: Lead initiatives to optimize cloud spend across the organization, including right-sizing, spot instance utilization, and cost attribution
• AI Cost Management: Design metering and budgeting for LLM token usage, and build cost-aware routing so workloads use the most efficient model for the job
• Visibility: Establish clear cost metrics and partner with engineering teams on architectures that are both performant and cost-effective
• Paved Roads: Build internal tools, CI/CD pipelines, and self-service platforms that make the reliable path the easy path for product engineering teams
• Toil Elimination: Identify systemic sources of toil across engineering and automate them away

Requirements:

• 10+ years of full-time software engineering experience in relevant roles (SRE, Infrastructure, Platform, DevOps, or Backend SWE), including significant time operating production systems at scale
• A track record of leading complex, multi-team infrastructure or reliability initiatives from ambiguity to delivery, and of influencing technical direction beyond your immediate team
• Strong coding skills in Golang, Python, or TypeScript, and a commitment to engineering best practices: design patterns, code reviews, and testing
• Deep, hands-on experience with AWS, Kubernetes, and Terraform
• Strong understanding of distributed systems, PostgreSQL, CI/CD systems, and observability tooling such as DataDog
• Hands-on experience implementing SLIs/SLOs, leading incident response, and running effective post-mortems
• Excellent written and verbal communication. You can write a clear design doc, explain trade-offs to executives, and build consensus across teams
• A strong desire to remove toil from systems, and the judgment to balance reliability, velocity, and cost
• Experience building Kubernetes operators
• Experience building or operating LLM toolchains, such as AI gateways, model routing (e.g., Bedrock, Anthropic), and governance frameworks, including PII scanning and audit trails for model interactions
• Demonstrated experience leading cloud cost optimization or building usage metering systems
• Experience in regulated industries (insurance, fintech, healthcare) or with compliance frameworks such as SOC 2
• Experience collaborating with globally distributed teams