senior-ml-engineer
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.
npx skills add majiayu000/claude-skill-registry --skill senior-ml-engineer-hainamchung-agent-assistant --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Senior ML/AI Engineer World-class senior ml/ai engineer skill for production-grade AI/ML/Data systems. ## Quick Start ### Main Capabilities ```bash # Core Tool 1 python3 ~/.{TOOL}/skills/senior-ml-engineer/scripts/model_deployment_pipeline.py --input data/ --output results/ # Core Tool 2 python3 ~/.{TOOL}/skills/senior-ml-engineer/scripts/rag_system_builder.py --target project/ --analyze # Core Tool 3 python3 ~/.{TOOL}/skills/senior-ml-engineer/scripts/ml_monitoring_suite.py --config config.yaml --deploy ``` ## Core Expertise This skill covers world-class capabilities in: - Advanced production patterns and architectures - Scalable system design and implementation - Performance optimization at scale - MLOps and DataOps best practices - Real-time processing and inference - Distributed computing frameworks - Model deployment and monitoring - Security and compliance - Cost optimization - Team leadership and mentoring ## Tech Stack **Languages:** Python, SQL, R, Scala, Go **ML Frameworks:** PyTorch, TensorFlow, Scikit-learn, XGBoost **Data Tools:** Spark, Airflow, dbt, Kafka, Databricks **LLM Frameworks:** LangChain, LlamaIndex, DSPy **Deployment:** Docker, Kubernetes, AWS/GCP/Azure *
- Quick Start
- Main Capabilities
- Core Expertise
- Tech Stack
- Reference Documentation
- 1. Mlops Production Patterns
- 2. Llm Integration Guide
- 3. Rag System Architecture
- Production Patterns
- Pattern 1: Scalable Data Processing
- Pattern 2: ML Model Deployment
- Pattern 3: Real-Time Inference
- Best Practices
- Development
Core Tool 1
python3 ~/.{TOOL}/skills/senior-ml-engineer/scripts/model_deployment_pipeline.py --input data/ --output results/
Core Tool 2
python3 ~/.{TOOL}/skills/senior-ml-engineer/scripts/rag_system_builder.py --target project/ --analyze
Core Tool 3
python3 ~/.{TOOL}/skills/senior-ml-engineer/scripts/ml_monitoring_suite.py --config config.yaml --deploy
Development
python -m pytest tests/ -v --cov
python -m black src/
python -m pylint src/What does the senior-ml-engineer skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill senior-ml-engineer-hainamchung-agent-assistant --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.