{"id":125202,"url":"https://alion.io/job/yuno-site-reliability-engineer-2","title":"Site Reliability Engineer","company":{"id":1872,"name":"Yuno","domain":"yuno.ai","url":"https://alion.io/company/yuno","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":93,"open_postings":11,"ghost_share":0,"stale_share":0.091,"repost_share":0,"time_to_fill_p50_days":67,"computed_at":"2026-10-17T05:46:00Z"}},"role":"DevOps","role_family":"DevOps","seniority":"staff","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["DE","FR","BE","IE","IT","PL","ES","SE","GR","PT"],"hiring_countries_total":10,"salary":null,"salary_estimate":{"min_usd":62000,"max_usd":143000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":823},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon EC2","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Chaos Engineering","optional":false},{"name":"Datadog","optional":false},{"name":"Docker","optional":false},{"name":"IAM","optional":false},{"name":"Kubernetes","optional":false},{"name":"NATS","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Pulumi","optional":false},{"name":"Python","optional":false},{"name":"RabbitMQ","optional":false},{"name":"Redis","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Amazon ECS","optional":true},{"name":"BigQuery","optional":true},{"name":"Braintrust","optional":true},{"name":"Databricks","optional":true},{"name":"Fly.io","optional":true},{"name":"Google BigQuery","optional":true},{"name":"Incident Management","optional":true},{"name":"Langfuse","optional":true},{"name":"LangSmith","optional":true},{"name":"LLM","optional":true},{"name":"MLFlow","optional":true},{"name":"Opsgenie","optional":true},{"name":"PagerDuty","optional":true},{"name":"Prefect","optional":true},{"name":"Replit","optional":true},{"name":"Snowflake","optional":true}],"status":"live","first_seen_at":"2026-08-10T02:31:07Z","employer_posted_date":"2026-08-10","last_verified_at":"2026-10-17T05:05:02Z","board_verified":true,"closed_at":null,"days_open":68,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":68},"description":"Remote · Full Time · Individual Contributor · +7 Years of Experience\nSite Reliability Engineer\nWho We Are\nYuno is the AI-native operating system of global commerce, powering the financial infrastructure of enterprise merchants, banks, and wallets. Through a single API, Yuno connects them to pay-ins, payouts, fraud prevention, KYC/KYB, and stablecoins globally, so they can operate everywhere. Agnostic by design and connected to 1,000+ payment methods and 460+ integrations in 190+ countries, Yuno optimizes acceptance rates, reduces costs, and strengthens security through specialized AI agents that learn from every transaction. Global brands including McDonald's, NetEase Games, GoFundMe, and Rappi run their payments on Yuno.\nAbout The Role\nYuno 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.\nThis 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.\nHow AI Shows Up in This Role\nThe 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\n\nAI 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\n\nYour Contribution Will Be\nReliability 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\n\nPlatform architecture and evolution - drive architectural decisions as the platform matures; the deciding voice on choosing technologies, designing systems, and when to evolve the infrastructure\n\nMessaging and event-driven architecture - design and own the messaging layer for inter-service communication, replacing synchronous patterns with durable, reliable async messaging\n\nInfrastructure and deployment - own the cloud infrastructure, automate provisioning with IaC, and ensure the platform scales reliably as transaction volume grows\n\nObservability - 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\n\nIncident 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\n\nChaos 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.\n\nWhat Success Looks Like\nWithin 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.\nSkills You Need\nMinimum Qualifications\nEvent-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\n\nDeep AWS - EC2, VPC, IAM, S3, and RDS - with strong networking fundamentals, since inter-service communication runs over the internal VPC\n\nInfrastructure as Code - Terraform or Pulumi, reviewed in PRs rather than clicked in consoles\n\nKubernetes and Docker in production - container lifecycle, resource limits, health checks, and orchestration at scale\n\nObservability 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\n\nChaos 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\n\nDistributed 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)\n\nDatabases - solid SQL (PostgreSQL) and NoSQL (MongoDB, Redis): when to use each, indexing, replication, and performance tuning\n\nProven technical leadership - you've set reliability standards, influenced architecture across teams, and mentored engineers, not just owned your own scope\n\nEnglish - advanced proficiency, written and spoken\n\nPreferred Qualifications\nAI / 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)\n\nMulti-tenant container platforms - running customer or user workloads in containers (Replit, Railway, Fly.io, or internal PaaS)\n\nData pipelines and orchestration - Airflow, Prefect, or similar; data warehouses like Databricks, Snowflake, or BigQuery a plus\n\nIncident management and on-call tooling - PagerDuty, Opsgenie, or incident.io\n\nExperience in the payments industry\n\nNice to Have\nECS experience\n\ns6-overlay for container process supervision\n\nExperience with AI agent framework ecosystems\n\nSpanish proficiency\n\nWhat We Offer at Yuno\nCompetitive Compensation\n\nRemote Work - you can work from everywhere\n\nHome Office Bonus - a one-time allowance to set up your ideal home office\n\nWork Equipment\n\nStock Options\n\nHealth Plan wherever you are\n\nFlexible Days Off\n\nLanguage, Professional, and Personal Growth courses\n\nDisclaimer\nWe 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. 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