Senior Infrastructure Engineer

The role

This remote role sits within the platform engineering group and focuses on the customer-facing production stack that handles very large traffic volumes. The engineer would own core cloud components across two major providers, shape shared infrastructure-as-code patterns, and help the team mature its operational culture as it grows internationally.

What you would do

• Run and advance the cloud platform that product teams rely on, spanning container compute, serverless, networking, and day-to-day cloud operations on two providers
• Establish and maintain infrastructure-as-code foundations and reusable modules that other engineering groups consume
• Engineer the internal network fabric linking services, prioritizing uptime, safety, and capacity headroom
• Elevate telemetry into the primary lens for operations by refining dashboards, paging rules, and reliability targets for owned systems
• Create AI-driven tooling for cloud management, such as automated policy checks, configuration drift spotting, and generated incident playbooks
• Contribute to team norms and practices as the group expands across regions

What they asked for

• Five or more years running cloud systems that serve customers with minimal tolerance for downtime
• Solid hands-on command of AWS and clear views on sound cloud operating practices
• Proven ability to run large Kubernetes estates, covering version upgrades, node pool handling, scaling policies, and add-on lifecycles
• Working knowledge of infrastructure-as-code frameworks such as Pulumi or Terraform, favoring code-driven change over manual console edits
• Experience operating workloads across multiple regions or across more than one cloud provider on AWS or GCP
• Openness to learning and visible willingness to adopt AI and newer technologies to speed up delivery

Nice to have

• Familiarity with Kubernetes-native autoscaling systems like Karpenter or the cluster autoscaler
• Hands-on use of observability stacks such as OpenTelemetry, Datadog, Prometheus, or Grafana
• Background building AI-supported infrastructure utilities for spend optimization, anomaly detection, or LLM-aided policy enforcement
• Involvement in multi-region designs covering data locality rules, failover between regions, or traffic steering by latency