Site Reliability Engineer

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. We care how you use it, not whether you do

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.

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.

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

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

ECS experience

s6-overlay for container process supervision

Experience with AI agent framework ecosystems

Spanish proficiency

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

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed or wish to exercise your data protection rights, please contact us at hiring@y.uno.