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Junior AI Model Version Compatibility Verification Developer

Aurora Vestergaard

Full-time · Job · Chicago

About the role

Deploying a new model version seems like it should be safe once the model passes accuracy tests. But a model that changed its output schema — returning a different confidence score format, adding a new class label, or dropping an optional field — can break the downstream applications that consume its predictions even when the model itself is more accurate. Compatibility verification catches these breaking changes before deployment reaches production. Our deployment platform manages model deployments for 45 enterprise ML teams. Version compatibility checking is a gap in our pre-deployment gate. We validate model performance against test sets. We don't systematically check whether the new version's output format, latency profile, and behavioral distribution are compatible with the downstream consumers that are registered for that model endpoint. As a junior developer, you'll build the model version compatibility verification components: output schema comparison between versions, behavioral distribution diff that detects meaningful changes in model output distributions, and the compatibility report generation that deployment engineers review before approving a rollout.

Responsibilities

  • Build output schema comparison detecting format changes between new and existing model versions
  • Implement behavioral distribution diff measuring output distribution changes across production traffic samples
  • Develop downstream consumer registry tracking which applications depend on each model endpoint version
  • Create compatibility report generation summarizing breaking changes and behavioral differences for deployment review
  • Build regression baseline comparison checking new model versions against established production behavior
  • Maintain compatibility checks as new model types and output formats are added to the platform

Requirements

  • 1+ year of Python and SQL development experience
  • Basic familiarity with REST APIs, data schemas, or software versioning concepts
  • Interest in ML platform engineering, DevOps, or software reliability
  • Attention to correctness in schema comparison and behavioral difference detection
  • Good communication for collaborating with deployment engineers on compatibility requirements

Benefits

  • Fully remote with flexible schedule
  • Health and dental insurance
  • $1,500 annual learning budget
  • Mentorship from senior MLOps engineers
  • 401(k) with match

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Categories

MLOps & AI Infrastructure Automation & AI Workflows

Skills

python docker sql fastapi airflow

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Job Type

Full-time

Level

Junior

Language

English

Salary Range

$60k – $80k / year

AI Expertise

MLOps & AI Infrastructure Automation & AI Workflows

Skills

python docker sql fastapi airflow statistics

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