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Xinle Xu
Junior Product Manager specializing in GenAI and global e-commerce
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字节跳动实习期间将内部AI重量预测模型从“可用但难上线”的单点能力，改造成可商业化复用的通用API：统一多地区接口与评估口径，设计分层兜底与置信度分级，先灰度上线SEA/JP并推动US/EU落地，结合线上结果进行模型微调。具备LLM/RAG/Agent系统的实战排障方法论，以及面向开发者与售前场景的技术演示与跨团队推进能力。
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Key Strengths
- 将既有模型能力产品化为可复用的通用API并实现商业化集成
- 以系统可靠性为核心的分层兜底与置信度机制设计（保证始终返回结果）
- 跨区域（SEA/JP→US/EU）灰度上线与规模化推广落地经验
- 跨团队统一评估口径与接口标准，化解业务对结果预期分歧
- LLM/RAG/Agent工作流的结构化诊断方法（复现-分层排查-止血优化）
- 面向开发者的技术演示能力：强调架构取舍、关键参数与可复现性
- 售前协作：将技术指标翻译为业务价值并降低客户决策不确定性
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