Lead, AI Engineer

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TheRoyalCaribbeanGroup’s

AI & Analytics Team has an exciting career opportunity for a full time Lead AI Engineerreporting to the Director AI Engineering

The position is onsite and based in Miami Florida

The position is also not eligible for work authorization sponsorship

Position Summary:

We are seeking a Lead AI Engineer to set engineering standards for Gen AI across Royal Caribbean Group and to lead delivery of projects that span multiple brands and business functions You will join the GenAI Solutions Engineering team within AI & Analytics and work closely with the GenAI Center of Excellence (CoE) You will also collaborate closely with business users to understand their requirements and quickly build prototypes they can react to

You will be a power user of agentic coding tools such as Claude Code and you will turn that fluency into shared skills plugins and standards that make every engineer and agent on the team faster and more reliable You will define the testing and evaluation practices that keep Gen AI applications and agents trustworthy in production then prove those practices on enterprise-wide initiatives by working with business stakeholders data engineers data scientists platform and security teams and product managers

Essential Duties and Responsibilities

AI-Enabled Engineering & Standards

Be a daily advanced user of agentic coding tools (Claude Code GitHub Copilot Cursor and similar) and set enterprise practices for them: context engineering project instruction files (CLAUDEmd / AGENTSmd) plan-then-implement workflows subagents parallel sessions hooks* and permission and sandbox settings

Design build and maintain skills plugins slash commands subagents hooks and MCP servers that encode our standards and workflows (for example scaffolding ticket creation PR review test and eval generation) and distribute them to engineers through a versioned* shared plugin catalog

Publish engineering standards as living agent-readable instructions that both people and coding agents follow and keep them short tested* and current as tools and models change

Define an AI-assisted delivery lifecycle: spec-driven development small reviewable changes automated AI-assisted code review and clear human accountability for everything that ships with guardrails for security secrets data classification* and IP

Establish reference architectures project templates and reusable accelerators that are agent-ready so teams across brands and functions start from a secure tested* production-ready baseline

Measure the impact of AI-assisted engineering (cycle time rework defect rates token cost) and use the evidence to refine tools prompts* and practices

Partner with the GenAI CoE to publish standards run design reviews* and retire patterns that no longer serve the enterprise

Testing & Quality Engineering

Set the testing strategy for Gen AI systems including unit integration contract end-to-end regression load and security testing for APIs retrieval pipelines agents* and tool integrations

Use coding agents to generate and maintain tests at scale* with human-reviewed intent and checks such as mutation testing to confirm the tests actually catch defects

Build test harnesses that handle non-deterministic behavior including mocked model and tool responses recorded fixtures* and deterministic seams around LLM calls

Build verification loops that let agents check their own work (tests type checks linters* and evals) before a human reviews it

Enforce automated quality gates in CI/CD (tests static analysis dependency and secret scanning AI-assisted review* and evaluation checks) so no agent or application ships without passing them

Lead blameless post-incident reviews and feed findings back into standards skills* and tests

Evaluation Frameworks

Design and own the enterprise evaluation framework for LLM applications and agents: golden datasets rubric- and model-graded evals human review workflows and task-completion groundedness hallucination safety latency* and cost metrics

Create evaluation approaches specific to RAG (retrieval precision and recall citation accuracy) tool-using agents (tool selection argument correctness trajectory quality)* and multi-agent workflows

Integrate evals into CI/CD as release gates and into production as continuous monitoring with clear thresholds dashboards* and regression alerts

Define and validate LLM-as-judge methods including calibration against human labels and bias checks* and guide teams on building representative evaluation datasets

Govern model prompt and configuration changes through versioned* evidence-based evaluation before promotion

Enterprise-Wide Delivery & Architecture

Lead technical design and delivery of Gen AI initiatives that span multiple brands business functions and data domains* including cross-domain agents and shared enterprise data and knowledge services

Collaborate closely with business users to understand their needs requirements and workflows and rapidly build working prototypes (using AI coding tools) that users can test and give feedback on* then iterate with them toward production-ready solutions

Architect and develop Gen AI applications and agents using Azure AI Foundry Azure Container Apps Azure AI Search* and Azure Databricks

Build full-stack solutions using Python FastAPI Nextjs and PostgreSQL and design agentic workflows with tool-use patterns and the Model Context Protocol (MCP) to connect agents to enterprise APIs data platforms* and third-party services

Design and optimize RAG pipelines including hybrid search semantic ranking* and vector indexing with Azure AI Search

Support and extend Microsoft Copilot Studio and Microsoft 365 Copilot solutions and advise when a low-code configurable* or custom-engineered approach is the right fit

Conduct feasibility studies and proofs of concept and turn successful ones into supported* production-grade services

Platform Security & LLMOps

Implement secure authentication and authorization using Microsoft Entra ID MSAL and service principals* and manage secrets with Azure Key Vault

• Manage APIs and microservices with Azure APIM and FastAPI

Lead CI/CD and infrastructure automation with Azure DevOps and GitHub including environment promotion release management* and rollback strategy

Apply LLMOps practices: prompt and model versioning deprecation planning tracing and observability drift detection* and cost and token monitoring using Azure Monitor and Log Analytics

Implement AI safety guardrails including content filtering prompt injection protection and output validation in line with enterprise security data classification* and responsible AI requirements

Leadership Governance & Collaboration

Mentor and coach engineers across teams on working effectively with AI coding agents and raise the bar for design code quality testing* and evaluation practice

Run hands-on enablement (workshops demos office hours) so skills and plugins are adopted* not just published

Contribute to the AI governance framework so that solutions align with enterprise AI principles compliance requirements* and responsible AI practices

Communicate technical trade-offs risks and progress clearly to engineering peers product owners* and senior leadership

Qualifications:

7+ years of professional software engineering experience* with 2+ years building and shipping Gen AI or LLM-based applications to production

Bachelor's degree in Computer Science Engineering or a related field* or equivalent practical experience

Advanced daily hands-on proficiency with agentic coding tools such as Claude Code including context engineering instruction files subagents hooks and parallel workflows* and the judgment to review and validate AI-generated code

Demonstrated experience building skills plugins slash commands subagents or MCP servers that other engineers adopt* and distributing them through a shared repository or catalog

Experience defining and driving engineering standards across multiple teams or products including reusable templates shared tooling* and agent-readable guidance

Strong Python (required) and TypeScript or another full-stack language* with the fundamentals to design sound APIs and architectures and to direct AI agents toward maintainable results

Hands-on testing expertise: unit integration contract and end-to-end testing (for example pytest) test automation in CI using AI to generate and maintain tests* and testing systems with non-deterministic components

Proven experience designing and operating LLM evaluation: building golden datasets defining metrics using LLM-as-judge and human review* and using evals to gate releases and monitor production quality

Deep understanding of Gen AI architectures including RAG tool use and function calling MCP* and single- and multi-agent orchestration

Proven expertise with Azure cloud services including Azure AI Foundry Container Apps AI Search APIM Key Vault and Monitor* plus containerization and infrastructure as code

Experience with Azure DevOps or GitHub for CI/CD release management* and environment promotion

Experience with enterprise identity management (Entra ID OAuth 20 service principals) secure coding practices and responsible AI implementation* including secure use of AI coding tools with enterprise data

Experience with observability and LLMOps: tracing prompt and model versioning monitoring* and cost management

Track record of partnering directly with business users to translate requirements into working prototypes delivering cross-functional or enterprise-scale projects with multiple stakeholders* and mentoring other engineers

Excellent problem-solving skills and the ability to work effectively in a fast-paced* dynamic environment

Strong written and verbal communication skills with the ability to explain technical trade-offs and standards to engineers business partners and senior leadership* Preferred Qualifications

Master's degree in Computer Science Engineering Data Science* or a related field

Experience with the Claude Agent SDK or similar frameworks and running coding agents headlessly in CI/CD for review testing* or automation

Experience operating an internal plugin or skills marketplace including versioning testing* and measuring adoption

Experience with Databricks (Unity Catalog Genie* MCP integrations) and enterprise semantic or data layers

Familiarity with Microsoft Copilot Studio Microsoft 365 Copilot extensibility* and Semantic Kernel

• Experience with evaluation and observability tooling for LLM applications and agents

Experience with multi-cloud Gen AI platforms (AWS Bedrock* Google Vertex AI)

• Experience with MCP server design and agent-to-agent interoperability patterns

Experience with AI safety testing such as red teaming prompt injection testing* and adversarial evaluation

• Experience establishing or contributing to an AI governance or engineering community of practice in a large enterprise

We know there's a lot to consider As you go through the application process our recruiters will be glad to provide guidance and more relevant details to answer any additional questions Thank you again for your interest in Royal Caribbean Group We'll hope to see you onboard soon!

It is the policy of the Company to ensure equal employment and promotion opportunity to qualified candidates without discrimination or harassment on the basis of race color religion sex age national origin disability sexual orientation sexuality gender identity or expression marital status or any other characteristic protected by law Royal Caribbean Group and each of its subsidiaries prohibit and will not tolerate discrimination or harassment

Nearest Major Market: Miami