[Hiring] Site Reliability Engineer @Yuno

Role Description

Yuno is looking for a Staff Site Reliability Engineer to set the technical direction for reliability across our infrastructure — starting with the platform that provisions, deploys, and manages AI agents at scale on AWS, the system powering payments across 190+ countries. The platform is in production and growing, and we need the most senior reliability voice in the room to evolve the architecture and make sure it stays reliable, observable, and ready to scale.

This is not a "maintain what exists" role, and it's not a single-system role. You'll own the reliability strategy — driving architectural decisions, designing event-driven communication, defining how we measure and defend reliability, and setting the standards other engineering teams build on.

How AI Shows Up in This Role:

• The platform you own is Yuno's AI agent infrastructure — provisioning and deploying AI agents at scale, plus the agents that route payments and prevent fraud.
• Keeping the AI-native layer reliable is the core of the role.
• AI is our default execution layer: you're encouraged to use AI-assisted tooling across automation, runbooks, incident analysis, and root-cause investigations, and to help define how the wider engineering org adopts it.

Your Contribution Will Be:

• Reliability strategy and standards — define the SLO culture, error-budget policy, and incident practices that scale across engineering teams, turning reliability from firefighting into a measurable, org-wide discipline.
• Platform architecture and evolution — drive architectural decisions as the platform matures; the deciding voice on choosing technologies, designing systems, and when to evolve the infrastructure.
• Messaging and event-driven architecture — design and own the messaging layer for inter-service communication, replacing synchronous patterns with durable, reliable async messaging.
• Infrastructure and deployment — own the cloud infrastructure, automate provisioning with IaC, and ensure the platform scales reliably as transaction volume grows.
• Observability — build the monitoring, tracing, and alerting that keeps the platform healthy; when something breaks at 3am, your dashboards and alerts should explain why before anyone has to dig.
• Incident leadership and mentorship — act as the senior escalation point for the hardest production problems, run blameless postmortems and root-cause analyses that turn into permanent fixes, and raise the reliability bar by mentoring senior and mid-level engineers.
• Chaos engineering mindset — continuous fault injection and resilience experiments that surface weaknesses before they turn into incidents, plus identifying and proposing resilience patterns to prevent those failures from reaching production.

What Success Looks Like:

• Within your first 6–12 months, you've set the reliability strategy for the platform, driven at least one major architectural evolution (event-driven messaging, streaming reliability, or observability), and engineering teams have adopted the SLO and error-budget framework you defined.
• You're the person Yuno trusts with the hardest reliability calls.

Qualifications

• Event-driven architecture and messaging systems — you've designed and owned systems around message queues (Kafka, NATS, RabbitMQ) and understand at-least-once delivery, consumer groups, dead letters, and backpressure; you've migrated a system from synchronous to async.
• Deep AWS — EC2, VPC, IAM, S3, and RDS — with strong networking fundamentals, since inter-service communication runs over the internal VPC.
• Infrastructure as Code — Terraform or Pulumi, reviewed in PRs rather than clicked in consoles.
• Kubernetes and Docker in production — container lifecycle, resource limits, health checks, and orchestration at scale.
• Observability and SLOs — Datadog fluency or equivalent (dashboards, monitors, APM, distributed tracing), and a track record defining and operating SLOs, SLIs, and error budgets across services.
• Chaos engineering and resilience testing — hands-on experience with fault injection, game days, or chaos experiments (Gremlin, Chaos Mesh, AWS FIS, or similar) to harden production systems.
• Distributed systems debugging — you've diagnosed async flows and cascading failures in production and can explain what broke and how you fixed it; comfortable coding for automation and tooling (Go, Python, or similar).
• Databases — solid SQL (PostgreSQL) and NoSQL (MongoDB, Redis): when to use each, indexing, replication, and performance tuning.
• Proven technical leadership — you've set reliability standards, influenced architecture across teams, and mentored engineers, not just owned your own scope.
• English — advanced proficiency, written and spoken.

Requirements

• AI / MLOps infrastructure — running AI workloads in production (model serving, LLM inference, GPU/resource management, and agent evaluation/observability tools like LangFuse, LangSmith, Braintrust, or MLflow).
• Multi-tenant container platforms — running customer or user workloads in containers (Replit, Railway, Fly.io, or internal PaaS).
• Data pipelines and orchestration — Airflow, Prefect, or similar; data warehouses like Databricks, Snowflake, or BigQuery a plus.
• Incident management and on-call tooling — PagerDuty, Opsgenie, or incident.io.
• Experience in the payments industry.

Benefits

• Competitive Compensation.
• Remote Work — you can work from everywhere.
• Home Office Bonus — a one-time allowance to set up your ideal home office.
• Work Equipment.
• Stock Options.
• Health Plan wherever you are.
• Flexible Days Off.
• Language, Professional, and Personal Growth courses.