Agentic workflows that take over support functions: intake, classification, context gathering, reproduction, diagnosis, response drafting, remediation, verification, and follow-up.
Tool-using agents that safely inspect runs, logs, auth state, configuration, and provider behavior, then propose or execute bounded recovery actions.
Eval suites for diagnostic correctness, resolution quality, safe escalation, customer communication, and end-to-end task completion.
Human-in-the-loop systems that make ownership, uncertainty, approvals, handoffs, and failure states explicit.
The observability, memory, and feedback loops that let these agents improve from real support cases without repeating mistakes.
Product improvements that remove entire classes of customer issues instead of handling the same symptoms faster.

Start with the customer outcome, then work backward into the model, workflow, tooling, or product change required to deliver it.
Own the hardest issues end to end when direct engineering is needed: reproduce, isolate, mitigate, ship, communicate, and verify the resolution with the customer.
Sit with customers and partner with Support, FDE, Product, and Engineering to find work that should become software.
Move from a rough prototype to a reliable production workflow, with traces, evals, guardrails, and a clear human fallback.
Use frontier models, coding agents, and internal AI tools every day to multiply your own engineering output.
Measure success in customer outcomes improved, support work removed, and classes of failure prevented.

Member Technical Staff - Applied AI Engineer

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