[Hiring] Senior Platform Engineer @Yuno

Role Description

Yuno is building a platform that provisions, deploys, and manages AI agents at scale on AWS. The platform is in production and growing. We need someone to own the infrastructure, evolve the architecture, and make sure the system is reliable, observable, and ready to scale.

This is not a "maintain what exists" role. You'll drive architectural decisions — designing event-driven communication, improving streaming reliability, building observability, and shaping the platform's infrastructure as it grows.

Your Contribution Will Be:

• Messaging and event-driven architecture — design and implement 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.
• Observability and reliability — 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.
• Platform evolution — evaluate and drive architectural decisions as the platform matures. Strong voice in choosing technologies, designing systems, and deciding when to evolve the infrastructure.

Qualifications

• Event-driven architecture and messaging systems — you've designed systems around message queues (Kafka, NATS, RabbitMQ, or similar).
• AWS — deep experience with EC2, VPC, IAM, S3, RDS.
• Databases — solid knowledge of both SQL (PostgreSQL) and NoSQL (MongoDB, Redis).
• Docker — container lifecycle, resource limits, health checks, bind mounts, multi-stage builds.
• Distributed systems debugging — you've debugged async flows and cascading failures across services in production.
• Infrastructure as Code — Terraform or Pulumi.
• Observability — Datadog fluency or equivalent.
• Tech Stack — hands-on experience with Go, AWS (EC2, S3, VPC, RDS PostgreSQL), Docker, PostgreSQL, MongoDB, Redis, and Datadog.

Requirements

• AI / MLOps infrastructure — experience running AI workloads in production.
• Multi-tenant container platforms — experience with platforms that run customer/user workloads in containers.
• Kubernetes — you've done the migration from "Docker on bare EC2" to K8s at least once.
• Data pipelines and orchestration — Airflow, Prefect, or similar.

Nice to Have

• ECS experience.
• s6-overlay for container process supervision.
• Experience with AI agent framework ecosystems.

Benefits

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