Start as a managed evaluation operations service for AI teams: audit current model workflows, define failure taxonomies, build golden datasets, create evaluation rubrics, and run recurring regression reports. The service delivers weekly failure analysis, curated data slices, benchmark harnesses, and recommendations for prompt, data, fine-tuning, or model changes. Over time, repeated templates for eval specs, dataset versioning, result tracking, and regression alerts can become a lightweight software layer around the service.

Production AI teams are moving from demos to deployed workflows where model regressions, safety gaps, latency tradeoffs, and domain failures have business consequences. The job signals show many companies are building internal eval/data flywheel roles, which creates room for an external specialist before every team can staff this function.