AI Engineer

Key Responsibilities:

• 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.

Requirements:

• Bachelor’s degree in Computer Science or equivalent professional experience
• 7+ years of professional software development experience
• 3+ years of professional AI engineering experience
• 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 (required): 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.
• Familiarity with one or more: .NET/C#, Go, Python, Java, React, MS SQL Server, Azure or AWS.
• 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 and ongoing operations.
• Code quality fundamentals: strong grasp of software design principles, automated testing, code review, and CI/CD.
• D3 (7+ yrs): independently delivers features with some guidance; strong fundamentals; beginning to make broader technical contributions.
• D4 (10+ yrs): fully autonomous; drives technical decisions within the team; mentors junior engineers.

Nice to Haves:

• 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 the US, China, and Latin America.