Your experience building ETL pipelines gives you a significant advantage in AI engineering. The core skills of extracting data from diverse sources, transforming it into usable formats, and loading it into destination systems are exactly what AI applications require at scale. Document processing, data quality validation, and pipeline orchestration are daily challenges in production AI systems. This path leverages your existing expertise in data extraction, transformation logic, scheduling, and monitoring to build AI-powered data pipelines. You understand data schemas, handling malformed records, and ensuring data quality, skills that directly apply to preparing training data and building RAG systems. ETL developers excel at AI engineering because they already think in terms of data flows and transformations. The transition focuses on applying your pipeline expertise to document processing, embedding generation, vector database ingestion, and retrieval-augmented generation. You will learn to build intelligent data pipelines that not only move data but understand and enrich it using LLMs. Your familiarity with tools like Airflow, dbt, or similar orchestration platforms translates directly to AI workflow automation. Timeline: 4-6 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