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AI Observability Engineer

Ulvex Systems

Remote · $130k – $160k / year · Full-time · Posted 5 months ago

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About the role

When a traditional API returns a 500 error, you know something broke. When an AI model starts giving subtly wrong answers, nobody notices until the damage is done. Model observability — catching quality degradation before users complain — is the hardest unsolved problem in production ML. We build observability tools for AI-native companies. Our platform monitors model predictions in real-time, detects distribution drift, tracks feature importance changes, and alerts engineering teams when quality drops below configured thresholds. Think Datadog for ML models. You'll work on the core detection engine: statistical methods for drift detection, anomaly scoring for prediction distributions, and automated root cause analysis that tells an engineer not just that something changed, but what changed and why.

Fully remote.

Responsibilities

  • Build real-time model observability and drift detection systems
  • Design statistical methods for detecting quality degradation in production models
  • Implement automated root cause analysis for model performance changes
  • Own the alerting and notification pipeline for ML quality incidents
  • Architect the observability platform for horizontal scaling across clients

Requirements

  • 5+ years in observability, monitoring, or production ML engineering
  • Strong statistical foundation for drift and anomaly detection
  • Kubernetes and GCP experience for platform infrastructure
  • Python proficiency with FastAPI for service development
  • Experience with MLflow or similar model management tools

Benefits

  • Fully remote — US time zones
  • Senior compensation with equity
  • $3,500 annual conference and learning budget
  • Solving one of the hardest problems in production ML

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Categories

MLOps & AI Infrastructure

Skills

python kubernetes docker gcp mlflow

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$130k – $160k / year

AI Expertise

MLOps & AI Infrastructure

Skills

python kubernetes docker gcp mlflow fastapi sql

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