Senior Manager, AI Engineering

About the role

Journey with us! Combine your career goals and sense of adventure by joining our exciting team of employees. Royal Caribbean Group is pleased to offer a competitive compensation and benefits package, and excellent career development opportunities, each offering unique ways to explore the world. We are proud to be the vacation-industry leader with global brands — including Royal Caribbean International, Celebrity Cruises and Silversea Cruises — the most innovative fleet and private destinations, and the best people. Together, we are dedicated to turning the vacation of a lifetime into a lifetime of vacations for our guests. The Royal Caribbean Group’s AI & Analytics Team has an exciting career opportunity for a full time Senior Manager, AI Engineering reporting to the Director, AI Engineering. The position is onsite and based in Miami, Florida. The position is also not eligible for work authorization sponsorship.

Responsibilities

• Leads the end-to-end design, engineering, and production operation of Generative AI and Agentic solutions - assistants, copilots, custom applications, and autonomous/human-in-the-loop agents - across various business domains like Guest Experience, Policy & Knowledge, HR/Talent, and back-office functions.
• Architects retrieval-grounded (RAG) systems and the enterprise context layer, knowledge bases, vector search, memory, and secure connectors (MCP) - so AI reasons over governed enterprise data, not just the base model.
• Guides the evaluation, selection, and operationalization of emerging AI capabilities, models, and platforms, balancing value against autonomy-driven risk and cost.
• Develops and executes strategies for integrating AI/GenAI technologies (LLMs, RAG pipelines, agents, and model-serving patterns) into new and existing products, ensuring global scalability and compliance.
• Directs large, complex, cross-functional programs, translating business requirements into scalable, secure, enterprise-grade AI systems with clear, measurable outcomes (productivity gained, revenue protected, cost avoided).
• Builds and champions reusable frameworks, patterns, and shared services that accelerate delivery and allow high-value point solutions to graduate into enterprise-grade assets.
• Collaborates with stakeholders to define requirements, prioritize the engineering backlog, and align AI solutions with organizational goals.