Agent skill · software engineering · alirezarezvani
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.
Why this skill is useful
Adds executable scripts and detailed workflows for deploying ML models and setting up MLOps pipelines that are not commonly found in public documentation.
What it needs
Requires docker, kubectl, kubernetes, mlflow installed locally. Requires openai account access. About 5k tokens when loaded. Last updated 2026-08-05. 23,939 stars on the source repository.
What this skill does
Senior ML Engineer Production ML engineering patterns for model deployment, MLOps infrastructure, and LLM integration. --- Table of Contents Model Deployment Workflow MLOps Pipeline Setup LLM Integration Workflow RAG System Implementation Model Monitoring Reference Documentation 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 Serving Options Option Latency Throughput Use Case -------- --------- ------------ ---------- FastAPI + Uvicorn Low Medium REST APIs, small models Triton Inference Server Very Low Very High GPU inference, batching TensorFlow Serving Low High TensorFlow models TorchServe Low High PyTorch models Ray Serve Medium High Complex pipelines, multi-model --- MLOps Pipeline Setup Establish automated training and deployment: 1. Configure feature store (Feast, Tecton) for training data 2. Set up experiment tracking (MLflow, Weights & Biases) 3. Create training pipeline with hyperparameter logging 4. Register model in model registry with version metadata 5. Configure staging deployment triggered by registry events 6. Set up A/B testing infrastructure for model comparison 7. Enable drift monitoring with alerting 8. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills alirezarezvani/senior-ml-engineer--7b3e7c