Architecture
AI Infrastructure Architect
The system that makes the system work at scale
Specializes in the compute and deployment layer. GPU clusters, inference optimization, latency budgets, MLOps pipelines, model versioning. Ensures the system works at scale — not just in the demo.
What this role covers
Inference optimization — Latency budgets, throughput, cost per call, caching strategies
MLOps pipelines — Model versioning, deployment, monitoring, drift detection
Scalability modeling — Designing for 10x and 100x before you need it
Cost architecture — Treating compute budget as a first-class design constraint
GPU & cloud infra — Cluster configuration, autoscaling, spot instances
When you need this role
High-volume consumer apps, fintech, healthcare
"Our inference costs are out of control. We have latency spikes we can't explain. We need someone who understands the compute layer, not just the model."
MLOps-immature companies post-prototype
"We have a great model in Jupyter. We have no idea how to run it in production for 50,000 users."