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.
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- 180
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/borghei/claude-skills --skill senior-ml-engineer- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads senior-ml-engineer automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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.
Tools
| Tool | Purpose | Command |
|---|---|---|
model_deployment_pipeline.py | Generate deployment artifacts (Dockerfile, K8s manifests, health checks) | python scripts/model_deployment_pipeline.py --input <path> --output <path> [--config <file>] |
rag_system_builder.py | Scaffold a RAG pipeline with vector store + retrieval logic | python scripts/rag_system_builder.py --input <path> --output <path> [--config <file>] |
ml_monitoring_suite.py | Set up drift detection, alerting, and dashboards | python scripts/ml_monitoring_suite.py --input <path> --output <path> [--config <file>] |
All tools support --verbose/-v and emit JSON (status, start_time, end_time, processed_items) to stdout. See references/tool-reference.md for full flag detail.
References
Load the reference that matches the task — keep this file lean and pull detail on demand:
- references/production-ml-workflows.md — the five step-by-step workflows (model deployment, MLOps setup, LLM integration, RAG, monitoring) with all code templates, serving/vector-DB/chunking/cost tables, the troubleshooting matrix, and success criteria. Read when executing any workflow.
- references/tool-reference.md — full flag/parameter tables and output formats for the three scripts. Read when scripting the tools.
- references/mlops_production_patterns.md — model deployment pipeline with Kubernetes manifests, feature store architecture with Feast examples, model monitoring with drift detection code, A/B testing with traffic splitting, automated retraining with MLflow. Read when building MLOps infra.
- references/llm_integration_guide.md — provider abstraction layer, retry/fallback with tenacity, prompt templates (few-shot, CoT), token optimization with tiktoken, cost calculation and tracking. Read when integrating an LLM.
- references/rag_system_architecture.md — RAG pipeline implementation code, vector database comparison/integration, chunking strategies, embedding model selection, hybrid search and reranking. Read when building a RAG system.
Scope & Limitations
This skill covers:
- End-to-end model deployment pipelines (packaging, containerization, serving, canary rollout)
- MLOps infrastructure setup (feature stores, experiment tracking, model registries, retraining)
- LLM integration patterns (provider abstraction, retries, caching, cost tracking)
- RAG system architecture (vector databases, chunking, retrieval, reranking)
This skill does NOT cover:
- Model training algorithms or hyperparameter tuning (see
senior-data-scientist) - Raw data pipeline construction and ETL orchestration (see
senior-data-engineer) - Prompt engineering techniques, few-shot design, or prompt optimization (see
senior-prompt-engineer) - Image/video model architectures or computer vision inference optimization (see
senior-computer-vision)
Integration Points
| Skill | Integration | Data Flow |
|---|---|---|
senior-data-scientist | Receives trained models and evaluation metrics for deployment | Data Scientist exports model artifacts and baseline metrics; ML Engineer packages and deploys |
senior-data-engineer | Consumes feature pipelines and data quality outputs | Data Engineer builds ETL and feature pipelines; ML Engineer reads from feature store for serving |
senior-prompt-engineer | Provides LLM serving infrastructure for prompt workflows | Prompt Engineer designs prompts; ML Engineer deploys provider abstraction and manages cost/latency |
senior-devops | Leverages CI/CD and Kubernetes infrastructure for model serving | DevOps manages cluster and pipelines; ML Engineer defines deployment manifests and health checks |
senior-computer-vision | Deploys vision models through shared serving infrastructure | CV Engineer trains and exports models; ML Engineer handles Triton/TorchServe deployment and monitoring |
senior-security | Applies security scanning to model containers and API endpoints | Security reviews container images and endpoint auth; ML Engineer remediates findings before promotion |
Last Updated: June 2026 Version: 1.1.0
FAQ
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.
How do I install senior-ml-engineer?
Run npx skills add https://github.com/borghei/claude-skills --skill senior-ml-engineer in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/senior-ml-engineer/ (personal) or .claude/skills/senior-ml-engineer/ (project).
Which AI agents can use senior-ml-engineer?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is senior-ml-engineer safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains senior-ml-engineer?
It is published by borghei in borghei/claude-skills. It has 180 recorded installs.
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