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AI Model Monitoring Engineer

Aubrey Okafor

Full-time · Job · Chicago

About the role

MLflow, Kubernetes, and a growing fleet of feature pipelines are what you'll be working with daily. If you want infrastructure work that's actually about machine learning rather than generic backend plumbing, this is that role. You'll build the monitoring layer that tracks model performance against the feature drift signals our platform already produces, catching degradation before a downstream team notices something is off. The models you'll ship monitoring for are used by teams across the company every day. Expect to work closely with the feature engineering team, since most of your alerts will be triggered by the drift signals they already generate.

Responsibilities

  • Build model performance monitoring on top of existing feature drift signals
  • Deploy monitoring services to Kubernetes alongside model serving infra
  • Track experiments and model versions in MLflow
  • Partner with the feature engineering team on alerting thresholds

Requirements

  • 3+ years in ML infrastructure or MLOps
  • Hands-on Kubernetes and AWS experience
  • Familiarity with MLflow for experiment and model tracking
  • Comfortable working with pandas for ad hoc analysis of alert data

Benefits

  • Fully remote position
  • Health insurance
  • 401k matching
  • Learning and conference budget

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Categories

MLOps & AI Infrastructure AI & Machine Learning Engineers

Skills

python mlflow kubernetes aws pandas

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$100k – $130k / year

AI Expertise

MLOps & AI Infrastructure AI & Machine Learning Engineers

Skills

python mlflow kubernetes aws pandas

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