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AI Deployment Dependency Graph Analyzer

Nathan Yamamoto

Full-time · Job · Chicago

About the role

A model doesn't deploy in isolation. It depends on feature pipelines, upstream data sources, downstream consumers, and shared infrastructure components — each of which has its own deployment state, version, and potential failure modes. Deploying a model without understanding its dependency graph is like pushing code without knowing what calls it. Our deployment platform manages 3,200 active model deployments across 45 enterprise clients. Dependency graph analysis is the capability that makes deployment safer: understanding which data pipelines feed a model, which downstream services consume its predictions, which infrastructure components it depends on, and what the blast radius looks like if any component in the graph changes. You'll build the dependency graph construction and analysis system: instrumenting the deployment platform to record dependency relationships automatically, building the graph analysis that surfaces transitive dependencies and circular dependency risks, and implementing the pre-deployment impact assessment that tells deployment engineers what could break before they push. The output is used in deployment review meetings; it needs to be trusted and actionable.

Responsibilities

  • Design automatic dependency relationship instrumentation during model registration and deployment
  • Build deployment dependency graph construction tracking data pipeline, infrastructure, and consumer relationships
  • Implement transitive dependency analysis identifying second and third-order deployment risks
  • Create pre-deployment impact assessment surfacing components at risk from a proposed change
  • Develop circular dependency detection flagging problematic architectural patterns
  • Build dependency graph visualization for deployment review and incident investigation

Requirements

  • 6+ years in ML platform engineering, DevOps, or distributed systems architecture
  • Experience with dependency management, service graphs, or deployment orchestration
  • Strong Python and Kubernetes skills with graph data structure familiarity
  • Understanding of ML deployment patterns and common dependency failure modes
  • Track record building developer tooling that earns trust through reliability and accuracy

Benefits

  • Fully remote with flexible schedule
  • Comprehensive health benefits
  • $4,000 annual professional development budget
  • Equity participation
  • Performance bonus

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Categories

MLOps & AI Infrastructure AI Product & Strategy

Skills

python docker kubernetes fastapi sql

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$130k – $160k / year

AI Expertise

MLOps & AI Infrastructure AI Product & Strategy

Skills

python docker kubernetes fastapi sql spark

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