AI Data Engineering
Data pipelines, feature stores, and embedding generation for AI/ML systems
Data pipelines, feature stores, and embedding generation for AI/ML systems
Optimizing vector embeddings for RAG systems through model selection, chunking strategies, and caching
Engineer effective LLM prompts using zero-shot, few-shot, chain-of-thought, and structured output techniques
Vector database implementation for AI/ML applications, semantic search, and RAG systems