Individual skill
senior-ml-engineer
Use for putting ML models or LLM features into production: deployment, MLOps, drift monitoring, RAG pipelines, LLM API cost and retries. Deploys with model_deployment_pipeline.py, monitors with ml_monitoring_suite.py and builds RAG with rag_system_builder.py.
engineering-team/skills/senior-ml-engineer/SKILL.md
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SKILL.mdcore · 9,512 bytesSKILL.mdcore · 9,512 bytesSKILL.mdcore · 9,512 bytesSKILL.mdcore · 9,512 bytesSKILL.mdcore · 9,512 bytesSKILL.mdcore · 9,512 bytesSKILL.mdcore · 9,512 bytesreferences/llm_integration_guide.mddoc · 8,900 bytesreferences/mlops_production_patterns.mddoc · 7,307 bytesreferences/rag_system_architecture.mddoc · 10,286 bytesscripts/ml_monitoring_suite.pyruns · 2,782 bytesscripts/model_deployment_pipeline.pyruns · 2,812 bytesscripts/rag_system_builder.pyruns · 2,777 bytes
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# Senior ML Engineer
Production ML engineering patterns for model deployment, MLOps infrastructure, and LLM integration.
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## 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)