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AI Deployment Dependency Risk Analyzer

Mia Levy

Full-time · Job · Chicago

About the role

Deploying a new model version is rarely just about the model. It touches the inference service, the feature pipeline that feeds it, the downstream services that consume its predictions, and the monitoring infrastructure that validates its behavior. Failing to understand these dependencies before deployment is how you turn a routine model update into a 2 AM incident. Our ML deployment platform serves 120 engineering organizations. The dependency risk analyzer we are building scans the deployment graph before each deploy — identifying all services that will be affected, checking for version incompatibilities, estimating blast radius if the new model produces unexpected output distributions, and flagging the specific dependencies that require manual validation before proceeding. You will build the dependency graph extraction and risk scoring engine: integrating with service registries, ML metadata stores, and deployment history to construct the impact graph, then applying risk models based on historical deployment incident patterns.

Responsibilities

  • Build dependency graph extraction from service registries and ML metadata stores
  • Implement risk scoring based on historical deployment incident patterns
  • Design blast radius estimation for output distribution changes
  • Create pre-deployment checklist generation based on identified dependency risks
  • Build integration with CI/CD pipelines for automated dependency analysis
  • Monitor deployment outcomes to improve risk model accuracy over time

Requirements

  • 3+ years in MLOps or platform engineering with ML deployment experience
  • Experience with service dependency mapping and deployment risk assessment
  • Strong Python and Kubernetes knowledge
  • Familiarity with ML metadata stores like MLflow or Weights & Biases
  • Statistical reasoning for risk scoring and anomaly detection

Benefits

  • Fully remote
  • Health and dental
  • $3,000 annual learning budget
  • Home office stipend
  • Equity participation

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Categories

MLOps & AI Infrastructure Automation & AI Workflows

Skills

python kubernetes docker aws mlflow

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$100k – $130k / year

AI Expertise

MLOps & AI Infrastructure Automation & AI Workflows

Skills

python kubernetes docker aws mlflow sql

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