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AI Model Endpoint Health Check Automation Engineer

David Dubois

Full-time · Job · UTC

About the role

We just crossed 400 model endpoints running across our customers' Kubernetes clusters, and right now the only way we find out one is degrading is when a customer complains. That's not sustainable, and it's the first thing we're fixing this quarter. You'll build automated health-check tooling that continuously probes model endpoints for latency drift, silent failures, and resource starvation, then routes alerts to the right on-call engineer before customers notice. This touches our core deployment platform, so the work ships fast and gets used immediately. Expect a high-velocity few months: designing the probe framework, wiring it into our existing MLflow-based deployment pipeline, and rolling it out cluster by cluster.

Responsibilities

  • Build synthetic health-check probes for deployed model endpoints
  • Integrate health signals into existing MLflow deployment and rollback workflows
  • Set up alert routing so the right engineer gets paged for the right failure
  • Reduce mean-time-to-detection for silent model degradation

Requirements

  • 3+ years working with Kubernetes-based deployment systems
  • Solid Python and Docker experience
  • Familiarity with MLflow or a comparable model deployment tool
  • Comfortable owning an on-call rotation for the systems you build

Benefits

  • Fully remote, async-friendly team
  • Health coverage stipend
  • Home office budget
  • Annual team retreat

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Categories

MLOps & AI Infrastructure Automation & AI Workflows

Skills

python docker kubernetes aws mlflow

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$100k – $130k / year

AI Expertise

MLOps & AI Infrastructure Automation & AI Workflows

Skills

python docker kubernetes aws mlflow

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