# LLM Engineering — MDRSS semantic catalog

> Semantic domain: llm-engineering
> Model training, serving, retrieval, evaluation, efficiency, alignment, and infrastructure.
> Filters: category=mlops-and-ml-systems
> Aggregate catalog URL: https://mdrss.com/catalog/llm-engineering?category=mlops-and-ml-systems

## Feeds

- [Data, Research & Open Knowledge](https://mdrss.com/s/data-research-and-open-knowledge) — Datasets, reproducible research, FAIR data, open knowledge, and analysis methods.
- [Software & Open Documentation](https://mdrss.com/s/software-and-open-documentation) — Open technical documentation, developer platforms, infrastructure, and tooling.

## Aggregate endpoints

- RSS: https://mdrss.com/catalog/llm-engineering/rss.xml?category=mlops-and-ml-systems
- JSON: https://mdrss.com/catalog/llm-engineering/feed.json?category=mlops-and-ml-systems

## Semantic domain cards (6)

### [Recsys Pipeline Architect](https://mdrss.com/llm-engineering/mlops-and-ml-systems/901385/901385.md)

A spec-and-scaffold skill for building composable recommendation, ranking, and feed pipelines. Encodes the six-stage pattern popularized by xAI's open-sourced For You algorithm (Apache 2.0) and applies it to any "top K for (user, context)" problem. Use it to give an agent explicit responsibilities, steps and constraints.

Feed: [llm-engineering](https://mdrss.com/s/llm-engineering) · Snapshot: 2026-08-04T13:48:56.862Z · Version: 1

### [ML Pipeline Workflow](https://mdrss.com/llm-engineering/mlops-and-ml-systems/901384/901384.md)

Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment. Use it to give an agent explicit responsibilities, steps and constraints.

Feed: [llm-engineering](https://mdrss.com/s/llm-engineering) · Snapshot: 2026-08-04T13:48:56.862Z · Version: 1

### [Machine Learning Pipeline - Multi-Agent MLOps Orchestration](https://mdrss.com/llm-engineering/mlops-and-ml-systems/901383/901383.md)

This workflow orchestrates multiple specialized agents to build a production-ready ML pipeline following modern MLOps best practices. The approach emphasizes:. Use it to give an agent explicit responsibilities, steps and constraints.

Feed: [llm-engineering](https://mdrss.com/s/llm-engineering) · Snapshot: 2026-08-04T13:48:56.862Z · Version: 1

### [Mlops engineer](https://mdrss.com/llm-engineering/mlops-and-ml-systems/901382/901382.md)

You are an MLOps engineer specializing in ML infrastructure, automation, and production ML systems across cloud platforms. Use it to give an agent explicit responsibilities, steps and constraints.

Feed: [llm-engineering](https://mdrss.com/s/llm-engineering) · Snapshot: 2026-08-04T13:48:56.862Z · Version: 1

### [Ml engineer](https://mdrss.com/llm-engineering/mlops-and-ml-systems/901381/901381.md)

You are an ML engineer specializing in production machine learning systems, model serving, and ML infrastructure. Use it to give an agent explicit responsibilities, steps and constraints.

Feed: [llm-engineering](https://mdrss.com/s/llm-engineering) · Snapshot: 2026-08-04T13:48:56.862Z · Version: 1

### [Data scientist](https://mdrss.com/llm-engineering/mlops-and-ml-systems/901380/901380.md)

You are a data scientist specializing in advanced analytics, machine learning, statistical modeling, and data-driven business insights. Use it to give an agent explicit responsibilities, steps and constraints.

Feed: [llm-engineering](https://mdrss.com/s/llm-engineering) · Snapshot: 2026-08-04T13:48:56.862Z · Version: 1