ai-engineer - AI Engineer for LLM Applications, RAG, and AI Agents
Builds production-ready LLM applications, advanced RAG systems, intelligent agents, and multimodal AI integrations.
Tags
Updated: 2026-09-19Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Integrate LLM models and serving frameworks
- Build advanced RAG systems
- Orchestrate multi-agent workflows and memory
- Implement vector search and embeddings
- Optimize prompt engineering and safety
- Deploy production AI systems
- Integrate multimodal AI models
- Enforce AI safety and governance
- Manage AI data pipelines
- Develop REST and GraphQL APIs
Inputs
- AI application requirements
- User queries and prompts
- Source documents and data
- API keys and model configurations
Outputs
- Production-ready AI system code
- System architecture designs
- RAG search results and responses
- AI system performance metrics
Requirements
- Access to LLM APIs or local inference servers
- Vector database instance
- Python runtime environment