Platform Infra Engineer - Cloud Reliability & Automation

CrewAI

United States

On-site

USD 130,000 - 210,000

Full time

2 days ago
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Job summary

CrewAI is seeking a Platform/Infrastructure Engineer to own and operate the cloud and enterprise deployment infrastructure. You’ll work across AWS, Azure, and GCP, handling containers, CI/CD, deployment automation, observability, secrets, networking, and reliability.

This role builds the internal platform enabling fast, scalable customer deployments. You’ll collaborate with runtime engineers and product teams, write automation in Python/Go, and help harden production environments while reducing

Qualifications

  • Production SaaS infra engineering experience.
  • Deep practical experience with AWS, Docker, CI/CD and containerized services.
  • Experience with ECS and/or Kubernetes; Helm is a plus.
  • Comfort operating PostgreSQL, Redis, queues, and web services in production.
  • Strong debugging across app, infra, network, deploy and dependencies.

Responsibilities

  • Own and improve infrastructure running the platform across cloud and on‑premise environments.
  • Build and maintain CI/CD pipelines for builds, tests, migrations, and deployments.
  • Improve reliability, incident response, capacity planning, and on‑call readiness.
  • Collaborate with runtime and product engineers on production behaviors.
  • Manage observability: logs, metrics, traces, dashboards, and alerting.

Skills

AWS
Docker
Kubernetes
CI/CD
Security
Python
Go
PostgreSQL
Redis

Tools

Helm
Terraform
OpenTelemetry

Job description

CrewAI is seeking a Platform/Infrastructure Engineer to own and operate the cloud and enterprise deployment infrastructure. You’ll work across AWS, Azure, and GCP, handling containers, CI/CD, deployment automation, observability, secrets, networking, and reliability.

This role builds the internal platform enabling fast, scalable customer deployments. You’ll collaborate with runtime engineers and product teams, write automation in Python/Go, and help harden production environments while reducing

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