This engineering case study examines CareerAI as a mastery-gated framework linking role-based learning, diagnostic skill feedback, staged assessment, interview rehearsal, digital certificates, and outbound job discovery. The supplied implementation inventory describes eight technical career pathways with eight topics per role, three gated Learn–Try–Build phases, three five-item assessment stages, adaptive prioritization of historically weak skills, a six-part interview simulator including voice interaction and sandboxed JavaScript execution, and owner administration. React, Express, SQLite, and browser storage divide the system into presentation, protected persistence, and client-side progress concerns. Question order is randomized with Fisher–Yates shuffling; scores and readiness levels use disclosed deterministic thresholds; interview answers are matched with concept dictionaries. No trained machine-learning system, live job-feed API, or cryptographically signed credential is established by the evidence. Thus, 'verified career deployment' is a proposed objective, not an observed placement or independently validated skill outcome. The provided inventory reports 19 passing automated tests, but an executable repository, raw execution logs, and participant studies were unavailable for independent verification; nine retained user-supplied screenshots were available as illustrative interface-state evidence. We therefore analyze documented functionality, mathematical boundary behavior, data and trust boundaries, and a reproducible testing protocol without claiming unmeasured learning gains or predictive accuracy. The contribution is a traceable system design, a formalization of its decision rules, and a critical identification of validity, usability, cross-device consistency, and security requirements for subsequent empirical work.