AI Deployment Dependency Graph Analyzer

Yalvex AI

Remote · $130k – $160k / year · Full-time · Posted 4 months ago

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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.

Fully remote.

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