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Open nowPosted 255 days ago

AI全栈开发工程师（B端平台 & B端产品）

01ai/index55 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen nowAI全栈开发工程师（B端平台 & B端产品）01ai/index · Beijing, Chinese Mainland

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on 01ai/index's own form.

The reply lands in your private mailbox

3×more interviews than doing it yourself with ChatGPT.

The clock on this job

Early applications get read.

12.1% of postings close within 7 days. Measured by our own scanner across the market. 01ai/index postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 255 days ago

01ai/index median: 1 days open

The posting

1. 构建与迭代B端Agent平台与产品能力：工作流编排（DAG / State Machine）、多智能体协作、会话与任务系统（异步/长任务）、记忆与上下文管理。 2. 负责 Agent 工具体系工程化：工具注册/版本与兼容策略、参数 Schema（JSON Schema/Pydantic）、鉴权与租户隔离、幂等/重试/限流、失败降级、回放与审计。 3. 落地企业级 RAG：多源数据接入（文档 / DB / ES / 内网系统）、切分与索引、混合检索（Milvus / PGVector + ES）、重排、引用溯源、增量更新、权限过滤（ACL / RBAC / ABAC）。 4. 负责全栈交付：React 界面（用户界面和管理界面）、Python 后端（API、队列、调度、权限、审计、配置）。 5. 负责模型接入与路由：公有云与自建推理的统一适配层（Provider / Adapter）、模型路由策略、降级与容灾、成本与延迟优化（缓存、批处理、并发控制、token 预算）。 6. 负责交付与运维工程化：SaaS 多租户与配额；私有化安装包（K8s / Helm + Docker Compose）、离线依赖（镜像 / 包 / 模型 / 向量库）打包、升级迁移与回滚预案。 7. 建立评测与可观测：离线评测集、线上指标（成功率 / 工具成功率 / 引用命中率 / 延迟 / 成本）、A/B与回归门禁；基于Tracing / Metrics / Logs 实现端到端定位与复现。

技术栈：Python / React / LangGraph / Milvus / PGVector / Elasticsearch / etc. 经验要求：5年+ 1. Python 后端扎实：FastAPI（或等价）、异步/并发、任务队列、系统设计与性能优化。 2. React 前端扎实：能交付ToB用户界面和管理界面的复杂交互（流程编排 / 可视化 / 调试面板），熟悉工程化与状态管理。 3. 熟悉 LangGraph 或同类状态机 / 编排思想，能实现：状态持久化、重试与补偿、分支与回滚、人工介入（HITL）。 4. 熟悉 Milvus / PGVector / ES 的检索体系与 RAG 工程，理解混合检索、重排与引用溯源的工程化落地。 5. 熟悉ToB关键能力：多租户隔离、RBAC / ABAC、审计日志、配置隔离、灰度/回滚、配额/限流（至少参与过落地）。 6. 具备私有化/离线交付能力：K8s/Helm 与 Docker Compose 双形态部署；离线镜像仓库、依赖与模型分发；升级兼容与数据迁移。 7. 安全意识强：Prompt / Tool Injection 防护、最小权限、数据脱敏、审计可追溯。 8. 熟练使用相关AI工具包括不限于： - 代码：Cursor / Claude Code用于重构、单测生成、接口、性能优化建议 - 调试：AI辅助日志分析与根因归类、Tracing 辅助定位、自动生成复现脚本等 - 文档：自动生成架构文档 / 接口文档与变更说明 - 交付：用 AI 辅助生成 Helm values 模板、部署检查清单、升级脚本与回滚预案 加分项 - 有自建推理 / 混合推理经验：vLLM / TGI / LMDeploy 等，了解并发、Batching、缓存、Fallback、模型热切换。 - 有插件化 / Connector 平台经验（企业系统集成、工具市场、版本兼容）。 - 有可观测与评测平台经验（OpenTelemetry、回放系统、LLM-as-judge 的稳定性控制）。 - 有企业级 SSO / AD / LDAP 集成、内网安全合规经验。

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