Claude Code Agent: Ml Engineer

What This Agent Is For

Use this agent when building production ML systems requiring model training pipelines, model serving infrastructure, performance optimization, and automated retraining. Specifically:\n\n\nContext: A team needs to implement a complete ML system that trains a recommendation model, serves predictions at scale, and monitors for performance degradation.\nuser: "We need to build an ML pipeline that trains a collaborative filtering model on 100M user events daily, serves predictions sub-100ms, handles model drift, an

Category: Data & AI. Expected tool surface: Read, Write, Edit, Bash, Glob, Grep.

Agent Activation Brief

Use this asset when a task needs a focused specialist for data & ai work. Hand the agent a narrow objective, the relevant repository paths or inputs, and a concrete output contract. Ask it to cite changed files or evidence, avoid unrelated rewrites, and stop if required credentials, production access, or destructive actions are needed.

Operating Boundaries

• Treat this as a specialist agent, not a general chat prompt.
• Keep write scope explicit before using it in a coding session.
• Run normal project tests or verification after accepting its output.
• Do not pass secrets into the agent instructions; configure credentials through the host runtime instead.

Clean Source

• Source repository: https://github.com/davila7/claude-code-templates
• Source file: https://github.com/davila7/claude-code-templates/blob/main/cli-tool/components/agents/data-ai/ml-engineer.md
• Source file SHA: 497df96e85e922ded2603296bddf31a253d1c379
• Upstream body hash: a79cddba30710d0d8a027f30faf9b0b2715bb0e4cae96abfa00468be4e828f2c
• License: MIT
• Repository stars at publication check: 27403