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AI Inference Autoscaling Policy Engineer

Aurora Vestergaard

Contract · Job · Los_Angeles

About the role

We just signed two customers whose traffic is spikier than anything we've handled. GPU nodes take minutes to warm up, and our current autoscaler reacts after latency has already blown past SLA. We need someone senior, quickly, for a four-month contract. We have solid deployment pipelines, a model registry and good metrics. What's missing is predictive scaling: policies that read queue depth, request mix and time-of-day patterns and add capacity before it's needed, without burning money overnight. You'll design those policies, test them against replayed traffic and roll them out across our clusters.

Responsibilities

  • Design predictive autoscaling policies for GPU inference workloads on Kubernetes
  • Build a traffic replay harness to compare policies on cost and latency
  • Roll out changes safely with canaries and clear rollback paths
  • Hand over documentation and dashboards the internal team can maintain

Requirements

  • Proven experience running GPU inference at scale on Kubernetes
  • Deep familiarity with HPA, KEDA or custom autoscaling controllers
  • Comfortable with AWS GPU instance economics
  • Able to start within two weeks

Benefits

  • Four-month contract, paid monthly
  • Fully remote
  • Flexible hours with Pacific overlap
  • Possible extension into a longer engagement

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Categories

MLOps & AI Infrastructure Automation & AI Workflows

Skills

kubernetes docker python aws mlflow

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Job Type

Contract

Level

Senior

Language

English

Salary Range

$200k+ / year

AI Expertise

MLOps & AI Infrastructure Automation & AI Workflows

Skills

kubernetes docker python aws mlflow

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