Principal Engineer - AI Platform

Job Expectations

• Define and own the AI platform architecture: retrieval infrastructure, model lifecycle, evals framework, guardrails, and GenAI product feature design.
• Lead the build of the shared AI platform layer consumed by all AI product features and reusable by internal business teams.
• Hands-on contributor: build production AI systems, write proofs of concept, and validate architecture through working software.
• Set and enforce technical standards for the AI Platform team; drive architecture reviews and model quality reviews.
• Cook with the Personalization team to define clear boundaries between the GenAI product layer and existing ML personalization infrastructure.
• Contribute AI platform-specific patterns and lessons into iHerb's shared AI-driven SDLC golden path.
• Drive the hardest cross-cutting technical decisions across multiple teams and shared platform services.
• Establish and evolve iHerb's AI-driven SDLC golden path: shared standards, Claude Code skills, guardrails, and automation patterns.
• Lead complex multi-team technical efforts by coordinating architecture reviews, aligning peer Principals and EMs, and resolving competing approaches.
• Mentor and raise the technical bar across the engineering organization through code review, architecture review, and direct coaching of senior engineers.
• Represent engineering in cross-functional conversations with product, data science, security, and infrastructure.
• Feed architectural decisions into the shared knowledge base so institutional knowledge compounds across the organization.

Knowledge, Skills and Abilities

• AI-driven SDLC (required): demonstrated use of AI-assisted development tools such as Claude Code, GitHub Copilot, or Cursor to ship production systems. Can articulate workflow changes, quality tradeoffs, and guardrail strategies.
• Architecture at scale: experience designing and evolving large-scale distributed systems across multiple teams and years: APIs, data pipelines, event-driven architectures, or high-traffic platforms.
• Cross-org technical leadership: track record of driving architectural standards, technical roadmaps, or platform initiatives that span multiple teams or organizations.
• Engineering quality mindset: deeply held opinions on code quality, observability, CI/CD, test automation, and maintaining velocity without accumulating hidden debt.
• Communication and influence: able to write clear architecture documents, present to technical and non-technical audiences, and build consensus without formal authority.
• Experience working in distributed teams across the US, China, and Latin America.
• Production experience designing and operating AI platform systems: RAG pipelines, vector search, embedding infrastructure, or retrieval-augmented applications at scale.
• Hands-on experience with LLM evaluation: building eval frameworks, defining quality metrics, and iterating on model and prompt quality using data.
• MLOps experience: model deployment, monitoring, lifecycle management, and cost governance in a production environment.
• Experience building and operating agentic systems using frameworks such as LangChain, LlamaIndex, or equivalent.
• GenAI in production is required; prototype or research-only experience is not sufficient at this level.
• High degree of accuracy and attention to detail
• Excellent organization skills and ability to multi-task

Equipment Knowledge

• Experience with Microsoft Office Suite (Word, Excel, PowerPoint)
• Experience with Google Business Suite (Gmail, Drive, Docs, Sheets, Forms) preferred

Experience Requirements

• Generally requires a minimum of 10+ years of software engineering experience, with a significant portion at senior, staff, or principal IC level.

Education Requirements

• Bachelor’s Degree in Computer Science or related field preferred, or a combination of education and equivalent work experience.