Sr Software AI Engineer

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Client is one of the world's largest e-commerce retailers of health, wellness, and beauty products, serving customers in more than 185 countries. With a catalog of over 30,000 products and global logistics infrastructure, they help millions of people live healthier lives every day. They are growing its engineering organization around an AI-first mandate: using AI not just as a product feature but as the foundation for how they build software, serve customers, and operate the business.

They are looking for a Sr. Software AI Engineer who will write production code, own features end-to-end from spec through deployment, operations, and observability, and participate in on-call rotation for what the team ships. There is no handoff to a separate ops or reliability function; engineers own the full lifecycle. Test automation is built into the shipping process using AI-driven tooling and the golden path.
Build the GenAI-powered product experiences and the shared AI platform infrastructure that powers them. This includes RAG pipelines over the catalog and customer reviews, LLM-driven personalization, a conversational Wellness Agent, agentic workflow systems, and the evals and MLOps layer that makes AI features production-grade and repeatable. Specializations within this track include: RAG and personalization, agent framework and tool use, evals and guardrails, and LLM application development for internal business functions such as marketing automation and BI agents.

What you will do:
Design, build, and operate production AI features: RAG pipelines, LLM-driven recommendations, conversational agents, or agentic workflow automation.
Build the shared AI platform layer: retrieval infrastructure, eval frameworks, model monitoring, guardrails, and observability.
Write LLM applications and integrations with marketing platforms, BI tools, or customer-facing product surfaces.
Evaluate model and feature quality using structured eval frameworks; iterate on prompts, retrieval strategies, and model selection using data.
Use AI-driven SDLC tooling such as Claude Code as a daily practice for both AI and non-AI code.
Coordinate with the Personalization team to align GenAI product features with existing ML personalization signals.
Document AI system design decisions, evaluation results, and operational lessons in the shared knowledge base.
Own the observability of AI systems you build: latency, cost, quality drift, and error rates; participate in on-call rotation and respond to production incidents.

8+ years of software engineering experience. Fully autonomous; drives technical decisions within the team; mentors junior engineers.
Python proficiency; comfortable building and operating production LLM applications.
Hands-on experience with at least one specialization: RAG and retrieval systems, LLM evaluation, agentic frameworks (LangChain, LlamaIndex, or similar), or LLM-based workflow automation.
Understanding of prompt engineering, context window management, and LLM output quality tradeoffs.
Familiarity with vector databases, embedding models, or semantic search.
AI-driven SDLC : hands-on experience shipping production code with AI-assisted development tools such as Claude Code, GitHub Copilot, or Cursor. The bar is not awareness; it is daily use in delivering real software.
Full-stack awareness: comfortable contributing across layers of the stack when needed; purely single-layer specialists are not the target profile.
Production ownership: experience owning features end-to-end from spec through deployment,

NICE TO HAVE
Exposure to MLOps tooling or model deployment pipelines.
Contributions to internal developer tooling, golden path standards, or SDLC process improvements.
Experience with e-commerce platforms, product catalogs, or high-traffic consumer applications.
Exposure to MLOps tooling or model deployment pipelines.
Experience working in distributed teams across different time zones / geographies.
Track record of documenting architectural decisions, writing RFCs, or contributing to engineering wikis.

Selected candidates will be invited to take part in several rounds of interviews.
The role is expected to be full time and ideal candidates should be looking for a long term engagement.
A background check will be required as part of the onboarding process.

客户是全球最大的健康、保健和美容产品电子商务零售商之一，服务遍及185多个国家的客户。凭借超过30,000种产品的目录和全球物流基础设施，他们每天帮助数百万人过上更健康的生活。他们正围绕AI优先的战略方针扩展其工程组织：不仅将AI作为产品功能，更将其作为构建软件、服务客户和运营业务的基础。

他们正在寻找一位资深软件AI工程师，该工程师将编写生产代码，端到端地负责功能从规格到部署、运维和可观测性的全过程，并参与团队所交付内容的on-call轮值。不存在向独立运维或可靠性团队的交接；工程师负责完整生命周期。测试自动化通过AI驱动的工具和golden path内置于交付流程中。
构建GenAI驱动的产品体验以及支撑它们的共享AI平台基础设施。这包括基于产品目录和客户评价的RAG流水线、LLM驱动的个性化、对话式Wellness Agent、agentic工作流系统，以及使AI功能达到生产级和可重复性的evals和MLOps层。该方向内的专业领域包括：RAG与个性化、agent框架与工具使用、evals与guardrails，以及面向内部业务职能（如营销自动化和BI agents）的LLM应用开发。

你将做什么：
设计、构建和运维生产AI功能：RAG流水线、LLM驱动的推荐、对话式agents或agentic工作流自动化。
构建共享AI平台层：检索基础设施、eval框架、模型监控、guardrails和可观测性。
编写LLM应用，并与营销平台、BI工具或面向客户的产品界面进行集成。
使用结构化eval框架评估模型和功能质量；利用数据迭代prompts、检索策略和模型选择。
将AI驱动的SDLC工具（如Claude Code）作为日常实践，同时用于AI和非AI代码。
与Personalization团队协调，使GenAI产品功能与现有ML个性化信号保持一致。
在共享知识库中记录AI系统设计决策、评估结果和运维经验教训。
负责你所构建AI系统的可观测性：延迟、成本、质量漂移和错误率；参与on-call轮值并响应生产事件。

8年以上软件工程经验。完全自主；在团队内推动技术决策；指导初级工程师。
精通Python；能够自如地构建和运维生产LLM应用。
至少在一个专业领域具有实操经验：RAG与检索系统、LLM评估、agentic框架（LangChain、LlamaIndex或类似框架）或基于LLM的工作流自动化。
理解prompt engineering、上下文窗口管理以及LLM输出质量权衡。
熟悉向量数据库、embedding模型或语义搜索。
AI驱动的SDLC：具有使用AI辅助开发工具（如Claude Code、GitHub Copilot或Cursor）交付生产代码的实操经验。标准不是了解，而是日常使用其交付真实软件。
全栈意识：能够在需要时跨技术栈各层做出贡献；纯粹的单一层专家不是目标画像。
生产所有权：具有端到端负责功能从规格到部署的经验，

加分项
接触过MLOps工具或模型部署流水线。
对内部开发者工具、golden path标准或SDLC流程改进有贡献。
具有电子商务平台、产品目录或高流量消费者应用的经验。
接触过MLOps工具或模型部署流水线。
具有跨不同时区/地域的分布式团队工作经验。
有记录架构决策、编写RFC或为工程wiki做贡献的过往记录。

入选候选人将被邀请参加多轮面试。
该职位预计为全职，理想候选人应寻求长期合作。
作为入职流程的一部分，将需要进行背景调查。

⚑ 岗位信息收录自公开招聘页（ATS 官方），以雇主官网为准