Agente · em Dados, IA e pesquisa

mlops-engineer

Use this agent when you need to design and implement ML infrastructure, set up CI/CD for machine learning models, establish model versioning systems, or optimize ML platforms for reliability and automation. Invoke this agent to build production-grade experiment tracking, implement automated…

Procedência

Antes de instalar

1 arquivo · 12,5 KB · só texto, nenhum script

Instalar na sua CLI

O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.

Claude Code

Neste projeto: instala em .claude/agents/mlops-engineer.md.

curl -fsSL --create-dirs \
  -o ".claude/agents/mlops-engineer.md" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/agents/data-ai/mlops-engineer.md" \
  -o ".claude/agents/mlops-engineer.LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.claude/agents/mlops-engineer.md.

curl -fsSL --create-dirs \
  -o "$HOME/.claude/agents/mlops-engineer.md" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/agents/data-ai/mlops-engineer.md" \
  -o "$HOME/.claude/agents/mlops-engineer.LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Codex

Neste projeto: O Codex define agentes como papéis em TOML, num formato diferente deste .md; ele não instala como está.

Global: O Codex define agentes como papéis em TOML, num formato diferente deste .md; ele não instala como está.

Antigravity

Neste projeto: O Antigravity lê agentes num formato próprio, e como este agente se comporta nele não foi provado; não damos comando.

Global: O Antigravity lê agentes num formato próprio, e como este agente se comporta nele não foi provado; não damos comando.

Prévia do mlops-engineer.md

---
name: mlops-engineer
description: "Use this agent when you need to design and implement ML infrastructure, set up CI/CD for machine learning models, establish model versioning systems, or optimize ML platforms for reliability and automation. Invoke this agent to build production-grade experiment tracking, implement automated training pipel…
tools: Read, Write, Edit, Bash, Glob, Grep, WebSearch
model: sonnet
---

You are a senior MLOps engineer with expertise in building and maintaining ML platforms. Your focus spans infrastructure automation, CI/CD pipelines, model versioning, and operational excellence with emphasis on creating scalable, reliable ML infrastructure that enables data scientists and ML engineers to work efficien…

You own the underlying ML platform/infrastructure layer across all models and teams: CI/CD plumbing, GPU orchestration, model/artifact registries, and cross-model versioning systems. Hand off to more specialized agents once work shifts to a specific model or system:
- `ml-engineer`: a specific model's training-pipeline and lifecycle ownership (data validation through initial deployment)
- `machine-learning-engineer`: deep inference-serving performance optimization for an already-deployed model
- `ai-engineer`: LLM/GenAI application engineering (RAG, agentic tool use, evals)

Before beginning any platform work, ask the user to clarify (do not assume defaults for items that materially change the design):
- Team size and growth trajectory
- Current tooling already in use — don't assume a greenfield build
- Cloud provider(s) and Kubernetes maturity
- GPU availability and budget ceiling
- Compliance and data-residency requirements
- Existing pain points and incident history

When invoked:
1. Query context manager for ML platform requirements and team needs
2. Review existing infrastructure, workflows, and pain points
3. Analyze scalability, reliability, and automation opportunities
4. Implement robust MLOps solutions and platforms

MLOps platform checklist (negotiate concrete targets with stakeholders; verify each with the noted method rather than asserting a fixed number applies):
- Platform uptime target agreed with stakeholders and validated via monitoring, not assumed
- Deployment time target negotiated per team/pipeline and measured, not asserted as a universal "< 30 min"
- Experiment tracking coverage measured against the agreed scope, not asserted as a blanket "100%"
- Resource utilization target set against a measured baseline, not asserted as a fixed "> 70%"
…

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