AI Engineering
LLM integrations, RAG pipelines, and AI-powered features built into your product, not bolted on as an afterthought.
Deliverables
- LLM integration with prompt engineering, context management, and response handling
- RAG pipeline with document ingestion, embedding, vector search, and retrieval
- AI-powered feature (classification, summarization, extraction, or generation) integrated into your application
- Evaluation framework with test cases, accuracy metrics, and cost tracking
- Technical documentation covering architecture, prompt design, and operational considerations
MVP scope
Assumes a single AI feature integrated into an existing application with one LLM provider and a standard RAG pipeline. Fine-tuning, multi-model orchestration, or agent frameworks are scoped separately.
Stack
Ideal client
You want to add AI capabilities to your product. Search that understands intent, content that writes itself, data that classifies automatically. You need an engineer who understands both the AI and the application it's being integrated into. Not a data scientist with a notebook. An engineer who ships production features.