Agent skill · software engineering · borghei
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.
Why this skill is useful
Adds 3 executable scripts for model deployment, RAG pipeline building, and monitoring that extend the AI's capabilities in ML engineering.
What it needs
Requires python installed locally. About 3k tokens when loaded. Last updated 2026-07-21. 462 stars on the source repository.
What this skill does
Senior ML Engineer Production ML engineering patterns for model deployment, MLOps infrastructure, and LLM integration. Core Capabilities Model deployment — export to ONNX/TorchScript/SavedModel, containerize, canary rollout, and serve via FastAPI, Triton, TF Serving, TorchServe, or Ray Serve with p95<100ms / error<0.1% gates. MLOps pipelines — feature stores (Feast/Tecton), experiment tracking (MLflow/W&B), model registry, A/B testing, and drift-triggered retraining. LLM integration — provider abstraction, retry/fallback with exponential backoff, token counting, response caching, cost tracking, and Pydantic output validation. RAG systems — vector database selection, chunking strategies, ingestion, retrieval, and reranking. Model monitoring — latency/error tracking, input drift detection (KS test, PSI), prediction-shift alerts, and automated retraining triggers. When to Use Deploying a trained model to production with canary rollout and monitoring. Standing up MLOps infrastructure (feature store, registry, retraining). Integrating LLM APIs with provider abstraction and cost control. Building a RAG pipeline (vector DB + chunking + retrieval + reranking). Setting up drift detection and model-health alerting. Clarify First Before generating artifacts, confirm these inputs. If any is unknown or vague, ASK — do not assume: [ ] Task — model deployment / RAG pipeline build / monitoring setup (selects the script and workflow) [ ] Serving target & rollout — container vs K8s and canary vs direct (drives the generated Dockerfile/manifests and health gates) [ ] Model or data interface — the input/output contract, and for RAG the corpus + vector store (shapes the scaffold) Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills borghei/senior-ml-engineer