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Junior AI Deployment Metrics Developer

Mia Levy

Freelance · Job · Chicago

About the role

We deploy AI models for 45 enterprise clients. Every deployment generates metrics: pre-deployment test results, canary analysis outcomes, rollout speed, rollback frequency, time-to-production, and post-deployment drift signals. All of that data exists. None of it is currently aggregated into the deployment health reports that our engineering leadership and clients actually want. This is a 5-month freelance engagement to build the deployment metrics dashboard system. You'll build the ETL pipelines that pull deployment event data from our CI/CD systems, compute deployment health KPIs for each client and model, and create the weekly reports that go to engineering leads. The senior engineers who built the deployment infrastructure will explain the data schema; you'll build the metrics layer on top. The deliverable is clear: a dashboard that any engineering manager can open on Monday morning and answer 'how did deployments go last week, and which clients or models need attention.' If you're a junior developer who wants to build something with a clear success criterion and a realistic scope, this is it.

Responsibilities

  • Build ETL pipelines pulling deployment event data from CI/CD and monitoring systems
  • Compute weekly deployment health KPIs per client and model type
  • Create automated weekly deployment report generation and delivery
  • Build anomaly detection for deployment health metrics that flag problems proactively
  • Maintain dashboard as new model types and deployment workflows are added
  • Document data sources, metric definitions, and dashboard architecture

Requirements

  • 1+ year of Python and SQL development experience
  • Basic experience with data pipelines and ETL concepts
  • Familiarity with dashboarding tools (any platform)
  • Interest in DevOps or deployment engineering
  • Reliable async communication and documentation habits

Benefits

  • Fully remote freelance engagement
  • Flexible schedule — 25-30 hours per week
  • Competitive hourly rate
  • Mentorship from senior MLOps engineers
  • Clearly scoped 5-month engagement with defined deliverable

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Categories

MLOps & AI Infrastructure Automation & AI Workflows

Skills

python docker sql fastapi airflow

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Job Type

Freelance

Level

Junior

Language

English

Salary Range

$60k – $80k / year

AI Expertise

MLOps & AI Infrastructure Automation & AI Workflows

Skills

python docker sql fastapi airflow pandas

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