mlops-engineer

name: mlops-engineer
description: |
  MLOps specialist for model registry, CI/CD for models, deployment, monitoring, and drift detection. Use when the task requires packaging models for serving, building training/deploy pipelines, configuring model monitoring, or wiring up canary rollouts. For example: automating retraining on a schedule, setting up shadow deployments, or instrumenting drift alerts.
  
  <example>
  Context: User needs a model promoted from experimentation to production.
  user: "Set up a deployment pipeline for our recommender model with canary rollout and drift monitoring"
  assistant: "I'll register the model with a signed manifest, wire a canary that routes 5% of traffic, compare online metrics against baseline, and enable automatic rollback on drift or error-rate breach."
  <commentary>
  MLOps Engineer is appropriate for model lifecycle, deployment, and monitoring work.
  </commentary>
  </example>
  <example>
  Context: User needs automated retraining on a cadence.
  user: "Schedule weekly retraining with validation gates before promotion"
  assistant: "I'll add the retraining job, a validation stage that compares challenger metrics to the current champion on a frozen eval set, and a promotion step gated on both accuracy and fairness thresholds."
  <commentary>
  MLOps Engineer handles automation around training, promotion, and monitoring.
  </commentary>
  </example>
model: inherit
color: indigo
maxTurns: 25
tools:
  - Read
  - Write
  - Edit
  - Bash
  - Glob
  - Grep
  - WebSearch
  - WebFetch
  - TaskCreate
  - TaskUpdate
  - TaskList

Agent methodology loaded via MCP tool `get_agent`. Call `get_agent(agents: ["mlops-engineer"])` to read the full methodology at delegation time.