AI Platform Engineer

Responsibilities

• The platform is boring. Agents run on a schedule and on events, state survives restarts, failed runs roll back cleanly, and every run can be reconstructed from its log.
• Nothing ships unevaluated. Every registered agent has a real suite with safety cases, the gate blocks regressions, and when someone challenges an output the answer is a test case rather than an argument.
• The dangerous action is impossible, not discouraged. An audit of any agent's credentials shows it cannot perform the writes it is not allowed to perform, and the audit log makes every consequential decision traceable.
• Teams pull the platform instead of being pushed. Agents get adopted because the reports are useful and the contact is rare and well-timed, and each team ends up with at least one workflow that runs automatically.
• The platform reports its own value. The work absorbed is measured and published, so the case for expanding it is made with data rather than enthusiasm.
• Own the evaluation layer, and switch on the gate that depends on it. Golden suites with behavioral assertions rather than intuition, judge criteria with a written rubric, safety cases that must pass on every run, and a CI gate that blocks a regression from merging. This is the precondition for every agent that does…
• Build and register the agent portfolio. Reporting, drafting, linting, triage and question-answering agents across the executive, team-lead and individual-contributor layers, plus a meta layer that observes the platform and improves it.
• Design how the system contacts people. A hard interruption budget per person, message bundling instead of a stream of pings, and a structure that gives something useful before it asks for anything. Adoption depends on this more than on any other single design choice.
• Own the platform tooling. The compiler and validator, inventory integrity, and the paths that distribute context and capabilities into the repositories and chat surfaces where work happens.
• Design evaluations, not spot checks. You build golden sets, write behavioral assertions, define judge rubrics, set pass thresholds and gate CI on the result. You can explain why "we reviewed a bunch of outputs and they looked good" is not evaluation.

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