Career progression that actually makes sense for AI infrastructure work:
Storage → OS → Database → Cloud Infrastructure (IaC-focused) → CI/CD → AI
This stack sequence creates a perfect foundation. Storage layer understanding means you know how to optimize data pipelines and model checkpoints. OS-level work translates to container orchestration and kernel tuning for GPU workloads. Database experience helps with vector stores and embedding management. IaC skills are critical for reproducible ML environments. CI/CD knowledge enables proper MLOps pipelines.
The synthesis: All these layers converge in production AI systems. You're not just prompting models - you're architecting the entire stack from data persistence through deployment automation. This is the difference between "AI enthusiast" and "can actually ship AI at scale."
Storage → OS → Database → Cloud Infrastructure (IaC-focused) → CI/CD → AI
This stack sequence creates a perfect foundation. Storage layer understanding means you know how to optimize data pipelines and model checkpoints. OS-level work translates to container orchestration and kernel tuning for GPU workloads. Database experience helps with vector stores and embedding management. IaC skills are critical for reproducible ML environments. CI/CD knowledge enables proper MLOps pipelines.
The synthesis: All these layers converge in production AI systems. You're not just prompting models - you're architecting the entire stack from data persistence through deployment automation. This is the difference between "AI enthusiast" and "can actually ship AI at scale."