AI Kubernetes Cost Attribution Developer

Kolvex Cloud

Remote · $130k – $160k / year · Contract · Posted 5 months ago

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About the role

Kubernetes makes it easy to run workloads. It makes it hard to know what each workload actually costs. Shared nodes, bursty resource usage, and the gap between requested resources and actually consumed resources mean that naive cost attribution is wildly inaccurate. The ML team thinks their monthly compute spend is $80K; the actual number is $210K. We build FinOps tooling for Kubernetes-heavy engineering organizations. Our cost attribution engine accurately allocates actual compute costs to the teams, services, and individual workloads responsible for them — using real consumption metrics rather than request-based approximations. This is a 12-month contract to redesign the attribution engine for a new cost model that handles spot instances, preemptible nodes, and committed use discount allocation — scenarios where the current engine produces systematically wrong numbers.

Fully remote.

Responsibilities

Redesign Kubernetes cost attribution for spot instances and preemptible node scenarios
Implement committed use discount allocation across workload portfolios
Build actual consumption tracking using real resource utilization rather than requests
Design attribution models for shared infrastructure costs like ingress and monitoring
Validate attribution accuracy against cloud billing statements
Create migration path from current attribution model with minimal client disruption

Requirements

5+ years in platform engineering with Kubernetes operations and FinOps experience
Deep knowledge of Kubernetes resource model, scheduling, and billing concepts
Strong Python skills and familiarity with Prometheus and Kubernetes metrics APIs
Experience with cloud billing APIs and cost allocation concepts
Ability to deliver a scoped migration project with minimal disruption to existing clients