{
  "schemaVersion": "0.1.0",
  "profileId": "default",
  "generatedAt": "2026-06-27T06:12:42.472Z",
  "profile": {
    "id": "default",
    "name": {
      "zh": "DossierKit 示例专家",
      "en": "DossierKit Demo Expert"
    },
    "headline": {
      "zh": "企业级 Agent 平台与大模型应用架构专家",
      "en": "Agent Platform & LLM Application Architect"
    },
    "summary": {
      "zh": "一个虚构示例档案：10 年工程与 AI 产品落地经验，专注将 LLM/Agent 从演示原型落到可评测、可审计、可扩展的企业核心流程。",
      "en": "A fictional demo profile with 10 years of engineering and AI product delivery experience, focused on turning LLM and Agent prototypes into evaluable, auditable, scalable enterprise workflows."
    },
    "yearsOfExperience": 10,
    "location": {
      "zh": "中国 / 可远程 / 可 relocation",
      "en": "China / Remote / Open to relocation"
    },
    "avatar": "avatar.png",
    "companies": [
      {
        "name": "Alibaba",
        "type": "work"
      },
      {
        "name": "SenseTime",
        "type": "work"
      }
    ],
    "targetRoles": [
      {
        "id": "agent-platform",
        "priority": 1
      },
      {
        "id": "llm-application-architect",
        "priority": 2
      },
      {
        "id": "posttraining",
        "priority": 3
      },
      {
        "id": "ai-fde",
        "priority": 4
      }
    ],
    "links": {
      "email": "demo@example.com",
      "github": "https://github.com/example",
      "linkedin": "https://linkedin.com/in/example",
      "maimai": "",
      "huggingface": "",
      "website": "https://example.com"
    },
    "availability": {
      "status": {
        "zh": "开放高质量 AI 平台、Agent 产品和应用算法机会",
        "en": "Open to high-quality AI platform, Agent product, and applied algorithm opportunities"
      },
      "preferredLocations": [
        "Shanghai",
        "Beijing",
        "Hangzhou",
        "Shenzhen",
        "Remote"
      ],
      "expectedPackage": {
        "zh": "面议，优先长期价值与技术影响力",
        "en": "Open, prioritizing long-term scope and technical impact"
      }
    },
    "localized": {
      "zh": {
        "locale": "zh",
        "name": "DossierKit 示例专家",
        "headline": "企业级 Agent 平台与大模型应用架构专家",
        "summary": "一个虚构示例档案：10 年工程与 AI 产品落地经验，专注将 LLM/Agent 从演示原型落到可评测、可审计、可扩展的企业核心流程。",
        "location": "中国 / 可远程 / 可 relocation",
        "availability": "开放高质量 AI 平台、Agent 产品和应用算法机会"
      },
      "en": {
        "locale": "en",
        "name": "DossierKit Demo Expert",
        "headline": "Agent Platform & LLM Application Architect",
        "summary": "A fictional demo profile with 10 years of engineering and AI product delivery experience, focused on turning LLM and Agent prototypes into evaluable, auditable, scalable enterprise workflows.",
        "location": "China / Remote / Open to relocation",
        "availability": "Open to high-quality AI platform, Agent product, and applied algorithm opportunities"
      }
    }
  },
  "roles": [
    {
      "id": "agent-platform",
      "path": "agent-platform",
      "title": {
        "zh": "Agent 平台架构师",
        "en": "Agent Platform Architect"
      },
      "headline": {
        "zh": "如何匹配 Agent 平台架构师岗位",
        "en": "Why this profile fits Agent Platform Architect roles"
      },
      "summary": {
        "zh": "适合需要把 Agent 从 Demo 推到生产的团队，重点覆盖 runtime、tool-use、评测、权限和企业集成。",
        "en": "A fit for teams moving Agents from demos to production, covering runtime, tool-use, evaluation, permissions, and enterprise integration."
      },
      "focusAreas": [
        "Agent Runtime",
        "Tool-use",
        "LLMOps",
        "Evaluation",
        "Permission & Audit",
        "Enterprise Integration"
      ],
      "relatedCases": [
        "enterprise-agent-platform"
      ],
      "relatedLabs": [
        "dpo-tool-use",
        "eval-harness"
      ]
    },
    {
      "id": "posttraining",
      "path": "posttraining",
      "title": {
        "zh": "后训练应用算法专家",
        "en": "Applied Post-training Algorithm Expert"
      },
      "headline": {
        "zh": "如何从 Agent 应用切入后训练算法",
        "en": "How Agent applications connect with applied post-training"
      },
      "summary": {
        "zh": "适合需要从线上 Agent 反馈构建偏好数据、评测集和训练闭环的团队。",
        "en": "A fit for teams that need to turn production Agent feedback into preference data, evaluation sets, and training loops."
      },
      "focusAreas": [
        "SFT",
        "DPO",
        "GRPO",
        "RLVR",
        "Data Flywheel",
        "Evaluation Harness"
      ],
      "relatedCases": [
        "posttraining-data-flywheel"
      ],
      "relatedLabs": [
        "dpo-tool-use"
      ]
    },
    {
      "id": "ai-fde",
      "path": "ai-fde",
      "title": {
        "zh": "AI Forward Deployed Engineer",
        "en": "AI Forward Deployed Engineer"
      },
      "headline": {
        "zh": "如何把复杂客户现场转成可交付 AI 产品",
        "en": "How complex customer contexts become shippable AI products"
      },
      "summary": {
        "zh": "适合需要同时理解业务流程、现场集成和 LLM 产品工程的团队。",
        "en": "A fit for teams that need business process fluency, field integration, and LLM product engineering in one role."
      },
      "focusAreas": [
        "Discovery",
        "Workflow Mapping",
        "Prototype to Production",
        "Customer Integration"
      ],
      "relatedCases": [
        "enterprise-agent-platform"
      ],
      "relatedLabs": [
        "eval-harness"
      ]
    }
  ],
  "metrics": [
    {
      "label": {
        "zh": "10 年",
        "en": "10 years"
      },
      "value": {
        "zh": "工程与 AI 落地经验",
        "en": "Engineering and AI delivery"
      },
      "description": {
        "zh": "覆盖平台架构、业务系统、LLM 应用和企业集成。",
        "en": "Across platform architecture, business systems, LLM applications, and enterprise integration."
      }
    },
    {
      "label": {
        "zh": "3 类闭环",
        "en": "3 loops"
      },
      "value": {
        "zh": "运行时 / 评测 / 数据",
        "en": "Runtime / Eval / Data"
      },
      "description": {
        "zh": "把 Agent 质量管理拆成可运行、可评估、可训练的闭环。",
        "en": "Separates Agent quality into runtime, evaluation, and training data loops."
      }
    },
    {
      "label": {
        "zh": "跨团队",
        "en": "Cross-functional"
      },
      "value": {
        "zh": "产品 + 算法 + 工程",
        "en": "Product + ML + Engineering"
      },
      "description": {
        "zh": "能够在需求、架构、评测和上线之间建立共同语言。",
        "en": "Builds shared language across requirements, architecture, evaluation, and launch."
      }
    }
  ],
  "experience": [
    {
      "company": "Enterprise AI Platform Team",
      "role": {
        "zh": "Agent 平台负责人",
        "en": "Agent Platform Lead"
      },
      "period": "2024 - 2026",
      "location": {
        "zh": "上海 / 远程协作",
        "en": "Shanghai / Remote"
      },
      "summary": {
        "zh": "负责企业 Agent Runtime、工具调用权限、评测体系和数据闭环，推动多个业务流程从 POC 进入生产。",
        "en": "Led enterprise Agent runtime, tool permissions, evaluation, and data loops, moving multiple workflows from POC to production."
      },
      "highlights": [
        {
          "zh": "设计任务编排、工具注册、权限审计、回放评测和灰度发布机制。",
          "en": "Designed task orchestration, tool registry, permission audit, replay evaluation, and staged rollout mechanisms."
        },
        {
          "zh": "建立 badcase 标注、偏好数据沉淀和 post-training 反馈闭环。",
          "en": "Built a badcase review, preference data, and post-training feedback loop."
        },
        {
          "zh": "把成功率、人工接管率、工具误调用率纳入上线门槛。",
          "en": "Made success rate, human handoff rate, and tool misuse rate part of launch gates."
        }
      ],
      "tags": [
        "Agent Runtime",
        "LLMOps",
        "Evaluation",
        "Tool-use"
      ]
    },
    {
      "company": "Large-scale SaaS Product Group",
      "role": {
        "zh": "技术架构师",
        "en": "Technical Architect"
      },
      "period": "2020 - 2024",
      "location": {
        "zh": "杭州",
        "en": "Hangzhou"
      },
      "summary": {
        "zh": "主导多租户 SaaS、工作流平台和数据集成架构，服务跨部门运营与客户成功团队。",
        "en": "Owned multi-tenant SaaS, workflow platform, and data integration architecture for operations and customer success teams."
      },
      "highlights": [
        {
          "zh": "把高频运营流程沉淀为可配置工作流，降低重复研发成本。",
          "en": "Converted repetitive operations into configurable workflows, reducing duplicate engineering effort."
        },
        {
          "zh": "建设可观测性、审计和 SLA 看板，提升跨团队排障效率。",
          "en": "Built observability, audit, and SLA dashboards to improve cross-team incident response."
        }
      ],
      "tags": [
        "SaaS",
        "Workflow",
        "Data Integration"
      ]
    }
  ],
  "skills": [
    {
      "id": "agent-platform",
      "title": {
        "zh": "Agent 平台工程",
        "en": "Agent Platform Engineering"
      },
      "description": {
        "zh": "从工具调用、状态管理、权限审计到运行时可靠性的端到端平台能力。",
        "en": "End-to-end platform capability across tool-use, state, permissions, audit, and runtime reliability."
      },
      "skills": [
        "Agent Runtime",
        "Tool-use",
        "MCP",
        "Workflow Orchestration",
        "Guardrails"
      ]
    },
    {
      "id": "evaluation",
      "title": {
        "zh": "评测与 LLMOps",
        "en": "Evaluation and LLMOps"
      },
      "description": {
        "zh": "把主观体验转成可回放、可比较、可上线门控的评测系统。",
        "en": "Turning subjective quality into replayable, comparable, release-gating evaluation systems."
      },
      "skills": [
        "Golden Set",
        "Replay Harness",
        "Regression Gates",
        "Observability"
      ]
    },
    {
      "id": "posttraining",
      "title": {
        "zh": "后训练数据闭环",
        "en": "Post-training Data Loops"
      },
      "description": {
        "zh": "从 badcase、偏好数据、SFT/DPO 实验到产品反馈闭环。",
        "en": "From badcases and preference data to SFT/DPO experiments and product feedback loops."
      },
      "skills": [
        "SFT",
        "DPO",
        "Preference Data",
        "Data Flywheel"
      ]
    },
    {
      "id": "architecture",
      "title": {
        "zh": "产品工程架构",
        "en": "Product Engineering Architecture"
      },
      "description": {
        "zh": "面向复杂业务系统的配置化、多租户、可观测和可扩展架构。",
        "en": "Configurable, multi-tenant, observable, and scalable architecture for complex business systems."
      },
      "skills": [
        "TypeScript",
        "Python",
        "Distributed Systems",
        "SaaS"
      ]
    }
  ],
  "content": {
    "zh": {
      "cases": [
        {
          "slug": "posttraining-data-flywheel",
          "title": "Agent 后训练数据飞轮",
          "summary": "把线上 Agent badcase 转成可评测、可标注、可训练的偏好数据闭环。",
          "tags": [
            "Preference Data",
            "DPO",
            "Evaluation"
          ],
          "featured": true,
          "url": "/zh/work/posttraining-data-flywheel"
        },
        {
          "slug": "enterprise-agent-platform",
          "title": "企业级 Agent 平台",
          "summary": "建设可评测、可审计、可扩展的企业级 Agent Runtime 与工具调用体系。",
          "tags": [
            "Agent Runtime",
            "Tool-use",
            "LLMOps",
            "Evaluation"
          ],
          "featured": true,
          "url": "/zh/work/enterprise-agent-platform"
        }
      ],
      "lab": [
        {
          "slug": "dpo-tool-use",
          "title": "DPO for Tool-use Preference",
          "summary": "用 chosen/rejected 偏好数据优化企业 Agent 的工具调用决策。",
          "tags": [
            "DPO",
            "Tool-use",
            "Preference Data"
          ],
          "featured": true,
          "url": "/zh/lab/dpo-tool-use"
        },
        {
          "slug": "eval-harness",
          "title": "Agent 回放评测框架",
          "summary": "用生产 trace 构建可复现的 Agent 回归评测，支撑 prompt、工具和模型变更。",
          "tags": [
            "Evaluation",
            "Replay",
            "Regression"
          ],
          "featured": true,
          "url": "/zh/lab/eval-harness"
        }
      ],
      "writing": [
        {
          "slug": "agent-evaluation-playbook",
          "title": "企业 Agent 评测手册",
          "summary": "把 Agent 评测从主观试用变成可回放、可分层、可发布门禁的工程系统。",
          "tags": [
            "Evaluation",
            "LLMOps",
            "Agent"
          ],
          "featured": true,
          "url": "/zh/writing/agent-evaluation-playbook"
        }
      ]
    },
    "en": {
      "cases": [
        {
          "slug": "posttraining-data-flywheel",
          "title": "Agent Post-training Data Flywheel",
          "summary": "Turned production Agent badcases into evaluable, labelable, trainable preference data loops.",
          "tags": [
            "Preference Data",
            "DPO",
            "Evaluation"
          ],
          "featured": true,
          "url": "/en/work/posttraining-data-flywheel"
        },
        {
          "slug": "enterprise-agent-platform",
          "title": "Enterprise Agent Platform",
          "summary": "Built an evaluable, auditable, scalable Agent runtime and tool-use layer for enterprise workflows.",
          "tags": [
            "Agent Runtime",
            "Tool-use",
            "LLMOps",
            "Evaluation"
          ],
          "featured": true,
          "url": "/en/work/enterprise-agent-platform"
        }
      ],
      "lab": [
        {
          "slug": "dpo-tool-use",
          "title": "DPO for Tool-use Preference",
          "summary": "Used chosen/rejected preference data to improve enterprise Agent tool-use decisions.",
          "tags": [
            "DPO",
            "Tool-use",
            "Preference Data"
          ],
          "featured": true,
          "url": "/en/lab/dpo-tool-use"
        },
        {
          "slug": "eval-harness",
          "title": "Agent Replay Evaluation Harness",
          "summary": "Built reproducible Agent regression evaluation from production traces for prompt, tool, and model changes.",
          "tags": [
            "Evaluation",
            "Replay",
            "Regression"
          ],
          "featured": true,
          "url": "/en/lab/eval-harness"
        }
      ],
      "writing": [
        {
          "slug": "agent-evaluation-playbook",
          "title": "Enterprise Agent Evaluation Playbook",
          "summary": "Turning Agent evaluation from subjective trial into replayable, layered, release-gating engineering.",
          "tags": [
            "Evaluation",
            "LLMOps",
            "Agent"
          ],
          "featured": true,
          "url": "/en/writing/agent-evaluation-playbook"
        }
      ]
    }
  }
}