AI Forward Deployed Engineer Principal

Carnival Corporation•Doral, FL

3h•Hybrid

About The Position

One of the best-known names in cruising, Princess is the world’s leading international premium cruise line and tour company, carrying millions of guests each year to hundreds of destinations around the globe. We give our guests the Medallion Class experience others simply can’t. The Love Boat promises something for everyone. We are looking to hire a Principal AI Forward Deployed Engineer. The Principal AI Forward Deployed Engineer is embedded directly with business units across the organization to quickly prototype, deploy, customize, and operationalize AI solutions that solve critical business problems. This role bridges the gap between the AI/Data team and the business—translating ambiguous challenges into working AI applications that deliver measurable value. This is a full-stack engineering role with deep backend expertise. You will design and build production-grade applications, APIs, and data pipelines that bring AI capabilities to life. Proficiency in modern application development, containerization, orchestration, and event-driven architectures is essential. Unlike traditional engineering roles, the Principal AI Forward Deployed Engineer operates at the intersection of technical execution and business problem-solving. You will work side-by-side with stakeholders in Guest Services, Revenue Management, Operations, Food & Beverage, and other functions to rapidly prototype, deploy, and iterate on AI solutions in real-world environments—including shipboard systems. This role requires a builder's mindset: someone who can scope a vague problem, architect a robust solution, write production-grade code, and ship it fast. You will bring field insights back to the core AI team, identifying reusable patterns and influencing the product roadmap based on what you learn in the field. Here’s a summary of what Princess is looking for in a Principal AI Forward Deployed Engineer. Is this you?

Requirements

Bachelor's degree in Computer Science, Software Engineering, Data Science or related field
Master's degree preferred but not required
AWS certifications (Solutions Architect, Developer Associate, Machine Learning Specialty)
Kubernetes certifications (CKA, CKAD)
Relevant AI/ML or cloud certifications
6-8+ years of software engineering experience with a focus on backend/full-stack development
4+ years deploying AI/ML solutions in production environments
Led/architected 3+ production AI systems
Strong hands-on experience with Docker, Kubernetes, and cloud-native architectures
Experience building and operating event-driven or streaming data systems (Kafka, Kinesis)
Track record of delivering technical solutions in ambiguous, fast-moving environments
Track record leading technical engagements with senior/executive stakeholders.
Mentored junior engineers; led cross-functional delivery teams.

Nice To Haves

Experience in a Forward Deployed Engineer, Solutions Engineer, or Technical Consultant role
Experience building generative AI applications, LLM integrations, or agentic AI solutions
Background in travel, hospitality, or cruise industry
Experience with shipboard or distributed/disconnected computing environments
Contributions to platform engineering, developer tooling, or infrastructure automation

Responsibilities

AI Application Development & Deployment: Architect, build, and deploy enterprise-grade AI-powered applications using modern backend technologies (Python, Node.js, FastAPI, Express). Design and implement robust APIs and microservices architectures that integrate AI/ML models—including LLMs and agentic systems—with business systems at scale. Lead containerization strategies using Docker and manage complex deployments via Kubernetes (EKS/ECS) with a focus on reliability, scalability, and performance. Design and implement event-driven architectures using Kafka or similar streaming platforms for real-time data processing and AI inference. Take full ownership of end-to-end delivery from technical scoping and architecture design through production deployment, monitoring, optimization, and ongoing operational excellence.
Rapid Prototyping & Problem Discovery: Deconstruct ambiguous, complex business problems into actionable AI solutions by deeply understanding operational context, system constraints, and stakeholder priorities. Rapidly build proof-of-concept applications using appropriate technology stacks to validate approaches and demonstrate business value. Architect scalable, production-ready solutions that account for performance, reliability, security, and maintainability from inception. Lead iterative development cycles based on user feedback, refining solutions until they deliver measurable, quantifiable business impact. Serve as a trusted advisor to business units on what is technically feasible and strategically valuable.
Stakeholder Engagement & Technical Translation: Serve as the senior technical point of contact and trusted advisor for business stakeholders during AI deployments. Communicate complex technical concepts—including architecture decisions, trade-offs, risks, and recommendations—to executive leadership and non-technical audiences with clarity and confidence. Lead cross-functional collaboration with data scientists, data engineers, platform teams, infrastructure teams, microservice teams, security, privacy, and product managers to ensure solutions meet rigorous technical standards and business objectives. Build and maintain strong relationships with business partners through consistent delivery, transparent communication, and a demonstrated commitment to their success.
Field Insights & Platform Feedback: Champion continuous improvement by bringing strategic learnings from field deployments back to the core AI/Data and Platform teams. Identify opportunities to improve tools, infrastructure, and reusable components that benefit the broader organization. Author and maintain comprehensive documentation including solution architectures, design patterns, and operational runbooks that enable knowledge transfer and accelerate future deployments. Proactively identify gaps in platform capabilities (CI/CD, observability, infrastructure, developer experience) and advocate for improvements with supporting business justification. Define and elevate engineering standards and best practices across the AI organization, mentor junior and mid-level engineers on these standards.