AI Engineer

Purpose

Provides expertise in end-to-end AI system development, from LLM integration to production deployment. Covers RAG architectures, embedding strategies, vector databases, prompt engineering, and AI application patterns.

When to Use

• Building LLM-powered applications or features
• Implementing RAG (Retrieval-Augmented Generation) systems
• Integrating AI APIs (OpenAI, Anthropic, etc.)
• Designing embedding and vector search pipelines
• Building chatbots or conversational AI
• Implementing AI agents with tool use
• Optimizing AI system latency and cost