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AI Post-Deployment User Impact Analyzer

Mia Levy

Full-time · Job · Chicago

About the role

Docker, Kubernetes, and MLflow are the core of what we build at Lumio Systems. If you've shipped model deployments that engineering teams actually trust, this role should feel familiar fast. We help ML teams answer one question after every deployment: did this actually help users, or did it just look good in offline metrics. You'll build the analysis layer that connects deployment events to downstream user behavior, closing the loop between what engineers ship and what users experience. You'll join a cross-functional squad with defined sprints, clear ownership, and weekly syncs with leadership, working alongside our platform reliability team on infrastructure that already tracks thousands of deployments a month.

Responsibilities

  • Build pipelines connecting deployment events to downstream user behavior metrics
  • Design impact scoring for post-deployment model performance
  • Integrate impact tracking into existing MLflow deployment workflows
  • Partner with platform reliability team on deployment infrastructure
  • Present impact findings in deployment review syncs

Requirements

  • Production experience with Kubernetes and Docker-based ML deployments
  • MLflow or equivalent experiment tracking experience
  • AWS infrastructure experience at scale
  • Track record connecting technical metrics to user-facing outcomes
  • Comfort working in a structured, sprint-based team

Benefits

  • Fully remote
  • Equity package
  • Health and dental insurance
  • 401k matching
  • Conference and training budget

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Categories

MLOps & AI Infrastructure

Skills

python docker kubernetes mlflow aws

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$130k – $160k / year

AI Expertise

MLOps & AI Infrastructure

Skills

python docker kubernetes mlflow aws

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