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AI Cloud Resource Rightsizing Recommender

Aiden Ferrari

Full-time · Job · Chicago

About the role

The average enterprise overprovisioned its cloud resources by 35% in the last year. The ops team picked instance sizes conservatively when provisioning to avoid performance incidents; usage patterns settled well below the provisioned capacity; nobody had time to revisit sizing decisions. A recommendation engine that continuously monitors utilization and suggests downsizing — with confidence intervals on the performance risk — changes this from a periodic audit into a continuous optimization. We build cloud cost optimization infrastructure serving 55 enterprise clients with $2B in combined annual cloud spend. Rightsizing recommendations are our highest-impact feature: a client that follows our recommendations typically reduces their compute bill 18-28% in the first 90 days. The challenge is confidence: recommending a downsize that causes a performance incident is worse than recommending nothing. You'll build the rightsizing recommendation system: modeling utilization patterns to predict headroom under load, estimating the performance risk of downsizing based on historical peak behavior, generating recommendations with explicit confidence levels, and building the feedback loop that learns from accepted and rejected recommendations.

Responsibilities

  • Build utilization pattern models predicting resource headroom across CPU, memory, and network dimensions
  • Implement performance risk estimation for proposed downsizing based on historical peak utilization
  • Design recommendation confidence scoring calibrated against realized performance outcomes
  • Create recommendation ranking by estimated savings and risk level for client prioritization
  • Build feedback loop learning from accepted and rejected recommendations to improve future accuracy
  • Develop savings tracking comparing projected vs. realized cost reduction per recommendation cohort

Requirements

  • 3+ years in cloud cost optimization, FinOps, or infrastructure analytics
  • Strong Python, SQL, and statistical modeling skills with AWS or GCP experience
  • Experience with time series modeling for resource utilization forecasting
  • Understanding of cloud instance types, pricing models, and performance characteristics
  • Ability to design recommendation systems that balance savings with operational risk

Benefits

  • Fully remote with flexible schedule
  • Health and dental insurance
  • $3,000 annual professional development budget
  • 401(k) with match
  • Equity options

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Categories

MLOps & AI Infrastructure AI Business, Marketing & Strategy

Skills

python sql spark aws statistics

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$100k – $130k / year

AI Expertise

MLOps & AI Infrastructure AI Business, Marketing & Strategy

Skills

python sql spark aws statistics fastapi

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