ai-engineer

Build and deploy production-grade AI/ML systems with MLOps pipelines.

Updated Apr 10, 2026

Search Tags:#mlops #ai #rag #evaluation #pipeline #model-serving

Ready to run

npx skills add https://github.com/Exia-thd/Digital-Nervous --skill ai-engineer-exia-thd

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ai-engineer
Source: https://github.com/Exia-thd/Digital-Nervous/tree/main/skills/ai-engineer
Command: npx skills add https://github.com/Exia-thd/Digital-Nervous --skill ai-engineer-exia-thd

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Enterprises need reliable, scalable AI/ML pipelines that move from model selection and fine‑tuning to serving, monitoring, and cost management, while ensuring quality and robustness.

Core Features & Use Cases

Model Benchmarking & Routing: Compare multiple models for cost, latency, and quality, and automatically route requests to the optimal provider.
Production‑grade RAG Pipelines: Hybrid search with reranking, TTL‑based embedding refresh, and evaluation metrics such as RAGAS.
MLOps Automation: End‑to‑end pipelines covering data preprocessing, training, versioned registries, A/B testing, and continuous monitoring.
Evaluation & Monitoring: Automated test suites, LLM‑as‑judge assessments, cost tracking, latency alerts, and quality drift detection.
Use Cases: Deploy a customer‑support chatbot, build a recommendation engine, or launch a large‑scale language model service with built‑in fallback and scaling mechanisms.

Quick Start

Ask the AI Engineer to design and launch a production‑grade MLOps pipeline for your new language model.

Frequently Asked Questions about ai-engineer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build a production-grade MLOps pipeline for a large language model?▼

To build a production-grade MLOps pipeline, you need end-to-end automation covering data preprocessing, training, versioned registries, A/B testing, and continuous monitoring. This ensures reliable, scalable AI/ML pipelines from model selection to serving.

What is the best way to optimize RAG pipelines for enterprise environments?▼

Optimizing RAG pipelines for enterprise environments involves implementing hybrid search with reranking, TTL-based embedding refresh, and evaluation metrics such as RAGAS to ensure retrieval quality and robustness.

How does model benchmarking and routing work for cost and latency optimization?▼

Model benchmarking and routing compares multiple models for cost, latency, and quality, automatically routing requests to the optimal provider to ensure scalable AI/ML systems with built-in fallback mechanisms.

Can I automate evaluation and monitoring for my AI model serving infrastructure?▼

You can automate evaluation and monitoring using automated test suites, LLM-as-judge assessments, cost tracking, latency alerts, and quality drift detection to maintain robust production-grade AI/ML systems.

When do I need automated evaluation suites for ML pipelines?▼

Automated evaluation suites are needed for ML pipelines when moving models to production, requiring continuous monitoring, cost tracking, and quality drift detection to ensure reliable, scalable enterprise AI services.