senior-ml-engineer
ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost optimization. Use when the user asks about deploying ML models to production, setting up MLOps infrastructure (MLflow, Kubeflow, Kubernetes, Docker), monitoring model performance or drift, building RAG pipelines, or integrating LLM APIs with retry logic and cost controls. Focused on production and operational concerns rather than model research or initial training.
Profile →npx skills add alirezarezvani/claude-skills --skill senior-ml-engineer --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 Engineer Production ML engineering patterns for model deployment, MLOps infrastructure, and LLM integration. --- ## Table of Contents - [Model Deployment Workflow](#model-deployment-workflow) - [MLOps Pipeline Setup](#mlops-pipeline-setup) - [LLM Integration Workflow](#llm-integration-workflow) - [RAG System Implementation](#rag-system-implementation) - [Model Monitoring](#model-monitoring) - [Reference Documentation](#reference-documentation) - [Tools](#tools) --- ## Model Deployment Workflow Deploy a trained model to production with monitoring: 1. Export model to standardized format (ONNX, TorchScript, SavedModel) 2. Package model with dependencies in Docker container 3. Deploy to staging environment 4. Run integration tests against staging 5. Deploy canary (5% traffic) to production 6. Monitor latency and error rates for 1 hour 7. Promote to full production if metrics pass 8. **Validation:** p95 latency < 100ms, error rate < 0.1% ### Container Template ```dockerfile FROM python:3.11-slim COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt COPY model/ /app/model/ COPY src/ /app/src/ HEALTHCHECK CMD curl -f http://localhost:8080/health || exit 1
- Table of Contents
- Model Deployment Workflow
- Container Template
- Serving Options
- MLOps Pipeline Setup
- Feature Store Pattern
- Retraining Triggers
- LLM Integration Workflow
- Provider Abstraction
- Cost Management
- RAG System Implementation
- Vector Database Selection
- Chunking Strategies
- Model Monitoring
python scripts/model_deployment_pipeline.py --model model.pkl --target staging python scripts/rag_system_builder.py --config rag_config.yaml --analyze python scripts/ml_monitoring_suite.py --config monitoring.yaml --deploy
What does the senior-ml-engineer skill do?
ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost optimization. Use when the user asks about deploying ML models to production, setting up MLOps infrastructure (MLflow, Kubeflow, Kubernetes, Docker), monitoring model performance or drift, building RAG pipelines, or integrating LLM APIs with retry logic and cost controls. Focused on production and operational concerns rather than model research or initial training.
How do I install it?
Run `npx skills add alirezarezvani/claude-skills --skill senior-ml-engineer --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 alirezarezvani/claude-skills, a repository with 23,791 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.