Senior Ml Engineer

This is production ML engineering at the senior level. You're getting deployment pipelines, MLOps tooling, model monitoring, and the full stack from PyTorch/TensorFlow to Kubernetes. It covers RAG systems, LLM integration, feature stores, and distributed processing with Spark and Kafka. The reference docs actually walk through production patterns, not just theory. Honest take: the performance targets (sub-100ms P95, 99.9% uptime) and the emphasis on monitoring, security, and cost optimization show this is aimed at real production systems, not notebooks. Use it when you need to move models from experiments to services that stay up.

skills.sh installs: 799
GitHub stars: 33.2K
last updated: Aug 21, 2026

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Install

$ npx -y skills add davila7/claude-code-templates --skill senior-ml-engineer --agent claude-code

Installs into .claude/skills of the current project.

Files

Senior ML/AI Engineer

World-class senior ml/ai engineer skill for production-grade AI/ML/Data systems.

Quick Start

Main Capabilities

# Core Tool 1
python scripts/model_deployment_pipeline.py --input data/ --output results/

# Core Tool 2  
python scripts/rag_system_builder.py --target project/ --analyze

# Core Tool 3
python scripts/ml_monitoring_suite.py --config config.yaml --deploy