LLM-Driven Cloud-Based Infrastructure Design

Artificial intelligence has become integral to the life sciences, enabling large-scale data analysis and accelerating discovery. However, deploying these analytical capabilities depends on cloud infrastructures that are often complex to design, validate, and operate. Current approaches rely heavily on manual, time-consuming configuration by experts. This paper explores how artificial intelligence, specifically Large Language Models, can support not only genomic data analysis but also the automated creation of the underlying computational infrastructure. We propose a methodology that translates naturallanguage user stories and requirements into cloud-architectural specifications and containerized deployments, bridging the gap between user intent and executable infrastructure. Preliminary results indicate that LLM-driven synthesis can reduce human effort in defining container relationships and deployment logic, advancing automation in cloud-native bioinformatics.