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AI Cost Optimization Analyst

Kolvex Cloud

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

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About the role

Most companies running AI in production have no idea what it actually costs. GPU instances spin up and never get terminated. Models serve traffic at 5% utilization. Training jobs run on p4d instances when p3 would do. We audit these setups and typically find 40-60% cost reduction opportunities. This is a 6-month contract for a senior engineer who can audit AI infrastructure costs, identify optimization opportunities, and implement changes. Each engagement is 3-4 weeks: profile the client's cloud usage, map AI workloads to resources, recommend changes, and measure the savings. You need to understand both the engineering (what do these GPU instances actually do?) and the economics (is this spend justified by the model's business value?). If you've managed AI infrastructure budgets before, you've probably done this analysis informally. We do it systematically.

Fully remote.

Responsibilities

  • Audit client AI infrastructure costs across AWS and GCP
  • Profile GPU utilization and identify idle or underused resources
  • Recommend and implement instance rightsizing and scheduling optimizations
  • Build cost monitoring dashboards and alerting for ongoing tracking
  • Deliver savings reports with verified cost reduction metrics

Requirements

  • 5+ years in cloud infrastructure or MLOps engineering
  • Deep AWS and GCP cost management experience
  • Understanding of GPU workloads and ML training/serving infrastructure
  • Python and SQL skills for cost analysis and reporting
  • Experience communicating technical recommendations to business stakeholders

Benefits

  • 6-month contract with renewal option
  • Fully remote
  • Premium contract rate
  • Diverse client engagements in AI infrastructure

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Job Type

Contract

Level

Senior

Language

English

Salary Range

$130k – $160k / year

AI Expertise

MLOps & AI Infrastructure AI Business, Marketing & Strategy

Skills

python aws gcp sql pandas docker

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