Electronic Arts

The Role

Electronic Arts creates games and experiences that inspire players around the world. Within EA's GDX Team, our team builds and operates the platforms, automation, and infrastructure that help game development teams work reliably and at scale.

This role is open to engineers coming from systems engineering, DevOps, SRE, MLOps, ML platform, data platform, or cloud infrastructure backgrounds.

You will report to the Senior Manager, Software Engineering.

What You Will Do

• You will build Kubernetes-based infrastructure supporting production services and AI/ML workloads.
• You will manage cloud infrastructure, GitOps, CI/CD, and infrastructure-as-code using technologies such as Kubernetes, Argo CD, Helm, Terraform, and Azure DevOps or GitHub Actions.
• You will support and evolve MLOps platforms such as Kubeflow and the infrastructure required for ML pipelines, evaluation workloads, and AI services.
• You will build monitoring, logging, dashboards, and alerts that improve platform observability and operational reliability.
• You will troubleshoot production issues across infrastructure, deployments, networking, configuration, certificates, secrets, and cloud services.
• You will participate in incident response, root-cause analysis, platform upgrades, and reliability improvements.
• You will automate repetitive infrastructure and operational workflows using Python, scripting, or similar tooling.
• You will partner with software, ML, data, and operations engineers to turn development workloads into reliable production services.
• You will create documentation, runbooks, and operational practices that make platforms easier for the broader team to support.

What We Are Looking For

• 8+ years of experience operating Kubernetes and containerized workloads in production environments.
• Experience with a major cloud platform such as Azure, AWS, or GCP.
• 5+ years of experience with CI/CD, GitOps, and infrastructure-as-code practices.
• Familiarity with technologies such as Argo CD, Helm, Terraform, Prometheus, Grafana, Datadog, or equivalent tools.
• Experience using Python, Bash, PowerShell, or similar languages for automation.
• Manage a defined infrastructure or platform area and work through technical clarity.

Past Experiences

• Experience with Kubeflow, MLflow, Databricks, SageMaker, Azure ML, Airflow, or similar ML/data platforms.
• Experience supporting AI or machine learning workloads in production.
• Experience with Kubernetes platform administration, upgrades, or lifecycle management.
• Experience with SRE, platform engineering, DevSecOps, or globally distributed production environments.

What Success Is

You will progressively take ownership of critical platform infrastructure, improve reliability and observability, reduce manual operational work through automation, and make the environment easier for the wider team to operate and evolve.