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skillboardWorld-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.
World-class senior ml/ai engineer skill for production-grade AI/ML/Data systems.
# 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
This skill covers world-class capabilities in:
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 Monitoring: MLflow, Weights & Biases, Prometheus Databases: PostgreSQL, BigQuery, Snowflake, Pinecone
Comprehensive guide available in references/mlops_production_patterns.md covering:
Complete workflow documentation in references/llm_integration_guide.md including:
Technical reference guide in references/rag_system_architecture.md with:
Enterprise-scale data processing with distributed computing:
Production ML system with high availability:
High-throughput inference system:
Latency:
Throughput:
Availability:
# Development
python -m pytest tests/ -v --cov
python -m black src/
python -m pylint src/
# Training
python scripts/train.py --config prod.yaml
python scripts/evaluate.py --model best.pth
# Deployment
docker build -t service:v1 .
kubectl apply -f k8s/
helm upgrade service ./charts/
# Monitoring
kubectl logs -f deployment/service
python scripts/health_check.py
references/mlops_production_patterns.mdreferences/llm_integration_guide.mdreferences/rag_system_architecture.mdscripts/ directoryAs a world-class senior professional:
Technical Leadership
Strategic Thinking
Collaboration
Innovation
Production Excellence
Copy a source-pinned command for your client. You run it yourself.
Destination: .claude/skills/senior-ml-engineer · pinned to the source commit
# Run from your project root
git clone https://github.com/davila7/claude-code-templates.git .skillboard-tmp
git -C .skillboard-tmp checkout 5cc17cf267132cc690dee82239b80a99ec82e4f8
mkdir -p ".claude/skills"
cp -r ".skillboard-tmp/cli-tool/components/skills/development/senior-ml-engineer" ".claude/skills/"
rm -rf .skillboard-tmpReview the source before running. This copies files into your project; it is not a one-click install and does not verify runtime safety.
sudo apt update && sudo apt install -y gitnpm install -g @anthropic-ai/claude-code# Run from your project root
git clone https://github.com/davila7/claude-code-templates.git .skillboard-tmp
git -C .skillboard-tmp checkout 5cc17cf267132cc690dee82239b80a99ec82e4f8
mkdir -p ".claude/skills"
cp -r ".skillboard-tmp/cli-tool/components/skills/development/senior-ml-engineer" ".claude/skills/"
rm -rf .skillboard-tmpDestination: .claude/skills/senior-ml-engineer
Scanner static-checks@0.1.0 · commit 5cc17cf26713. Static checks cannot prove runtime safety – review the source and the exact diff before installing. How checks work.
No static rules matched. This is not a safety guarantee.