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AI Deployment Cost Governance Engineer

Rolvex Solutions

Remote · $130k – $160k / year · Full-time · Posted 3 months ago

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About the role

You'll be the fourth infrastructure engineer on a team of eleven, reporting into our platform lead. We already have solid deployment reliability tooling — canary rollouts, blue-green switching, health checks — but nobody owns what those deployments actually cost across clusters. What's missing is a governance layer: budget thresholds per team, automatic alerts when a deployment pattern is burning more GPU or compute than its baseline, and a clear approval path before a costly rollout ships to production. Your role is to build that layer on top of our existing Kubernetes and MLflow infrastructure, working with team leads to set sane thresholds and with the platform lead on weekly syncs to review what's tripping alerts and why.

Fully remote.

Responsibilities

  • Build cost governance rules on top of existing deployment infrastructure
  • Set and maintain budget thresholds per team in coordination with team leads
  • Create alerting for deployments that exceed baseline compute cost
  • Present weekly cost trends and anomalies in platform sync
  • Partner with the deployment reliability team to avoid duplicating tooling

Requirements

  • Hands-on experience with Kubernetes and container orchestration at scale
  • Familiarity with MLflow or a comparable experiment and deployment tracking tool
  • Comfortable working across AWS, GCP, or Azure billing APIs
  • Has built cost or budget alerting systems before

Benefits

  • Remote-first with quarterly team syncs
  • Stock options and 401k matching
  • Health, dental, and vision insurance
  • Defined sprint cycles and clear ownership boundaries
  • Weekly leadership syncs so you always know what's next

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Categories

MLOps & AI Infrastructure

Skills

kubernetes docker mlflow aws gcp

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$130k – $160k / year

AI Expertise

MLOps & AI Infrastructure

Skills

kubernetes docker mlflow aws gcp azure

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