AI & ML Engineering

About

This skill streamlines the development of sophisticated AI applications by providing standardized patterns for LLM integration, retrieval-augmented generation (RAG), and model optimization. It guides developers through the latest AI stack, including tools like DSPy for programmatic prompting, LangGraph for stateful workflows, and LlamaIndex for data ingestion. Whether you are fine-tuning models with LoRA, setting up vector databases like Qdrant, or implementing MCP-based tool sets, this skill ensures best practices in AI/ML engineering and MLOps are followed throughout the development lifecycle.

Key Features

• Automated evaluation using RAGAS and experiment tracking with MLflow
• Programmatic prompt optimization using DSPy modules
• 0 GitHub stars
• Production-ready RAG architectures with LlamaIndex and LangGraph
• Efficient model fine-tuning patterns using LoRA and QLoRA
• Model Context Protocol (MCP) integration for tool management

Use Cases

• Automating prompt engineering via programmatic optimization modules
• Building high-performance RAG pipelines for proprietary document sets
• Deploying specialized AI agents using stateful multi-agent workflows