ModelBridge provides a managed layer for packaging models and agents behind stable APIs, connecting them to backend services, and monitoring production performance. It focuses on deployment workflows, serving configuration, throughput optimization, and integration handoffs between ML, data, and application engineering teams.

AI teams are moving beyond prototypes into customer-facing products, creating urgent demand for infrastructure that productionizes models and agents reliably. The repeated hiring signals across AI infrastructure, LLMOps, document understanding, and integrations roles indicate this is an active operational bottleneck.