Job Summary: The Sr. Software Engineer II - AI will lead the build of GenAI-powered product experiences and the shared AI platform infrastructure that powers them. This includes RAG pipelines over the iHerb 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. Job Expectations: 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.

Responsibilities

described above may provide only a partial description of this position. This is not an exhaustive list of all aspects of the job.

not outlined in this document may be added as necessary or desirable, with or without notice. Knowledge, Skills and Abilities: Required: 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, observability, and on-call. Engineers here own what they ship; there is no separate ops handoff.

Code quality fundamentals: strong grasp of software design principles, automated testing, code review, and CI/CD. 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.

High degree of accuracy and attention to detail Excellent organization skills and ability to multi-task Preferred: 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 globally distributed teams. Track record of documenting architectural decisions, writing RFCs, or contributing to engineering wikis.

Requirements

: Generally requires a minim