Sr. Software Engineer II - AI

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.

Knowledge, Skills and Abilities

• 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.