Offer a fixed-scope ML^? pipeline hardening engagement that audits the current model lifecycle, then implements the missing production pieces: dataset/version tracking, training orchestration, model registry, CI/CD, inference deployment, monitoring, alerting, rollback, and cost visibility. Start as a hands-on managed service using existing customer cloud and ML^? tools rather than building a new platform from scratch. Over time, reusable Terraform modules, deployment templates, runbooks, and observability packs can become a repeatable productized service.

The signals show broad hiring demand for ML^? infrastructure as companies move from AI prototypes to production systems. Generative AI, multimodal models, and real-time inference are increasing complexity, cost pressure, and reliability expectations faster than many teams can staff internally.