Leverage your deep database expertise to become an AI engineer specializing in data-intensive AI systems. SQL developers possess a unique advantage in AI engineering, you understand how to structure, query, and optimize large datasets, which is fundamental to building retrieval-augmented generation (RAG) systems and AI applications that need to access enterprise data. Your experience with query optimization translates directly to optimizing vector searches and semantic retrieval. Knowledge of database design helps you architect efficient embedding storage solutions using pgvector and other vector-enabled databases. This path focuses on extending your SQL expertise into the AI domain: you'll learn how vector databases work alongside traditional RDBMS, how to implement semantic search using PostgreSQL extensions, and how to build production RAG systems that combine your database skills with modern AI capabilities. The transition emphasizes Python for AI tooling while maintaining your strengths in data management. By the end, you'll be able to design and implement AI systems that efficiently retrieve and process information from large-scale databases, a critical skill as enterprises adopt AI solutions that need to work with their existing data infrastructure. Timeline: 5-7 months.
Skills You'll Build
Skills You'll Build
Skills You'll Build
Skills You'll Build
Skills You'll Build
Skills You'll Build