AI原生技术负责人 | 自主系统

AI-Native Tech Lead | Agentic Systems

AI开发工程限定地区（需当地身份）

公司Alongside

薪资$20 - $25

工作地点Vietnam

地域资格限定地区（需当地身份）

时区要求日间重叠约 8 小时，基本正常作息

用工类型Contractor

发布时间11 天前

数据来源Himalayas

前往 Himalayas 查看并投递 →

注意地域限制：该职位明确限定在 Vietnam 招聘。如果你是位于中国大陆的求职者，通常需要当地工作身份才能投递，或需与雇主确认是否接受独立合同（Contractor）形式合作。

与组织合作推动数字化转型并打造高绩效技术团队
我们正在寻找一位实战型技术负责人，负责构建生产级AI代理并从架构到部署全程主导交付。您将编写代码、做出技术决策，并帮助团队快速交付可靠的AI驱动产品

职责：

使用Hermes Agent、OpenClaw、Letta、Agent Zero、OpenHands或类似开源运行时构建代理系统

使用OpenAI Agents SDK、LangGraph、Google ADK、Vercel AI SDK、CrewAI、AutoGen或Mastra等框架

开发可重用技能、工具、子代理、工作流、持久化内存、定时任务和消息集成

通过工具调用、结构化输出、流式传输和模型路由集成OpenAI、Anthropic、Gemini、开源权重和本地模型

使用MCP、A2A、REST API、webhooks、浏览器、终端和计算机使用工具将代理连接到系统

使用pgvector、Pinecone、Weaviate、Elasticsearch或同等技术构建RAG和上下文系统

通过LangSmith、Braintrust、Phoenix、OpenTelemetry或供应商追踪建立评估和可观测性

使用沙箱、最小权限访问、审批门禁、密钥管理、审计日志和提示注入防御来保护自主代理

使用Codex、Claude Code、Cursor或GitHub Copilot加速交付

负责技术计划、生产质量、延迟、可靠性和模型成本

要求
· 强大的软件工程和实战技术领导经验

有将AI代理或LLM应用投入生产的经验

强大的Python或TypeScript技能以及云基础设施经验

熟悉代理编排、记忆、工具使用、RAG、评估和人机协同设计

了解当前趋势，如自我改进代理、可重用代理技能、本地优先和自定义模型运行时、多模态和计算机使用代理、MCP/A2A互操作性、持久执行和评估驱动开发

福利

远程工作（越南基地）

全职：每天8小时，与葡萄牙营业时间一致（WET/WEST）

自由职业/服务协议

小时费率：20美元-25美元

最初发布于Himalayas

查看英文原文

Alongside partners with organizations to drive digital transformation and build high-performing tech teams.
We’re looking for a hands-on Tech Lead to build production-grade AI agents and lead delivery from architecture to deployment. You’ll code, make technical decisions, and help the team ship reliable AI-powered products quickly.

Responsibilities:

Build agent systems with Hermes Agent, OpenClaw, Letta, Agent Zero, OpenHands, or similar open-source runtimes.

Use frameworks such as OpenAI Agents SDK, LangGraph, Google ADK, Vercel AI SDK, CrewAI, AutoGen, or Mastra.

Develop reusable skills, tools, subagents, workflows, persistent memory, scheduled tasks, and messaging integrations.

Integrate OpenAI, Anthropic, Gemini, open-weight, and local models through tool calling, structured outputs, streaming, and model routing.

Connect agents to systems using MCP, A2A, REST APIs, webhooks, browsers, terminals, and computer-use tools.

Build RAG and context systems using pgvector, Pinecone, Weaviate, Elasticsearch, or equivalent technologies.

Establish evaluations and observability with LangSmith, Braintrust, Phoenix, OpenTelemetry, or provider tracing.

Secure autonomous agents using sandboxing, least-privilege access, approval gates, secrets management, audit trails, and prompt-injection defenses.

Use Codex, Claude Code, Cursor, or GitHub Copilot to accelerate delivery.

Own technical plans, production quality, latency, reliability, and model costs.

Requirements
· Strong software engineering and hands-on technical leadership experience.

Experience shipping AI agents or LLM applications into production.

Strong Python or TypeScript skills and cloud infrastructure experience.

Practical knowledge of agent orchestration, memory, tool use, RAG, evaluations, and human-in-the-loop design.

Familiarity with current trends such as self-improving agents, reusable agent skills, local-first and bring-your-own-model runtimes, multimodal and computer-use agents, MCP/A2A interoperability, durable execution, and eval-driven development.

Benefits