AI Engineer, Product
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The role
Contribute to the product team's LLM evaluation framework by designing reference tests, heuristics, and model-graded checks.
Define and monitor metrics such as task success, helpfulness, safety flags, latency, and cost to drive data-driven decisions.
Plan and execute A/B tests for prompts, models, and system prompts, with clear rollout or rollback recommendations.
Implement end-to-end observability for LLM calls, including structured logging, tracing, dashboards, and alerts.
Manage model release processes with canary/shadow traffic, sign-offs, SLO-based rollback criteria, and regression detection.
Improve core behaviors (memory policies, intent classification, follow-ups, routing, and tool-call reliability) and create reusable eval templates for teams.
Define and monitor metrics such as task success, helpfulness, safety flags, latency, and cost to drive data-driven decisions.
Plan and execute A/B tests for prompts, models, and system prompts, with clear rollout or rollback recommendations.
Implement end-to-end observability for LLM calls, including structured logging, tracing, dashboards, and alerts.
Manage model release processes with canary/shadow traffic, sign-offs, SLO-based rollback criteria, and regression detection.
Improve core behaviors (memory policies, intent classification, follow-ups, routing, and tool-call reliability) and create reusable eval templates for teams.
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