Infrastructure Engineer

Synthesia Synthesia · Multimodal · EUROPE · Engineering

Experienced DevOps/SRE/Platform Engineer to join the Cloud Infra team, responsible for maintaining and scaling Kubernetes (EKS) clusters, managing AWS/GCP environments, owning CI/CD systems (GitHub Actions), defining Infrastructure as Code (Terraform/Terragrunt), strengthening observability (Datadog), and driving FinOps practices in a fast-moving AI video company. The role focuses on enabling product engineers to build and deploy AI technologies.

What you'd actually do

  1. Maintain and scale Kubernetes (EKS) clusters — managing workloads, deployments, and monitoring at production scale.
  2. Manage and evolve our AWS (and some GCP) cloud environments, balancing reliability, cost, and velocity.
  3. Own and improve our CI/CD systems (GitHub Actions on our self-hosted AWS runners).
  4. Define and implement Infrastructure as Code using Terraform and Terragrunt.
  5. Strengthen observability via Datadog and enable teams to understand their systems in production.

Skills

Required

  • DevOps / SRE / Platform experience
  • Kubernetes
  • AWS or GCP
  • Terraform / Terragrunt
  • Linux
  • Python scripting
  • CI/CD design patterns
  • Datadog or similar observability tooling

Nice to have

  • Temporal.io or workflow orchestration frameworks
  • Frontend development
  • Tooling development
  • React
  • Node.js
  • Supporting AI research or data-intensive environments

What the JD emphasized

  • Deep hands-on DevOps / SRE / Platform experience
  • Strong Kubernetes experience
  • Proven AWS and or GCP expertise
  • Proficiency with Terraform / Terragrunt
  • Strong understanding of CI/CD design patterns
  • Experience with Datadog or similar observability tooling
  • Comfortable operating autonomously in ambiguous environments
  • bias toward execution and written communication

Other signals

  • AI video platform
  • state-of-the-art technologies
  • fast-moving AI company
  • deploy and monitor production services
  • supporting AI research or data-intensive environments