World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.
Quick integration into your workflow with minimal setup
Active open-source community with continuous updates
MIT/Apache licensed for commercial and personal use
Customizable and extendable based on your needs
Download or copy the skill file from the source repository
Place the skill file in Claude's skills directory (usually ~/.claude/skills/）。
Restart Claude or run the reload command to load the skill
Tip: Read the documentation and code carefully before first use to understand functionality and permission requirements
All Skills from open-source community, preserving original authors' copyrights
alirezarezvani__claude-skills/engineering-team/senior-ml-engineer/skill.mdProven benefits and measurable impact
Reduce manual story decomposition time from hours to minutes per Epic
Accelerate sprint execution with consistently sized, well-researched stories
Eliminate rework by ensuring stories meet organizational requirements upfront
Perfect for these scenarios
Quickly decompose large epics into 5-10 ready-to-implement stories with context
Replan story sets when scope changes or new requirements emerge mid-sprint
Auto-match stories to team capabilities and discovered Epic context
Ensure stories comply with org standards via automated research delegation