Lead, AI Engineer

Royal Caribbean Group; Miami (FL); On-site; USD 180,000 - 240,000; Full time

Job summary

Royal Caribbean Group is seeking a Lead Gen AI engineer to set standards across the enterprise, define delivery frameworks, and guide teams on agentic coding and LLMOps across brands and functions.

You will join the GenAI Solutions Engineering team in AI & Analytics, collaborating with business users to rapidly prototype solutions and establish production-ready baselines and governance. This role is onsite in Miami and interfaces with the GenAI CoE.

Qualifications

• Bachelor’s degree in Computer Science or a related field or equivalent practical experience.
• 7+ years of software engineering experience with Gen AI/LLM deployments.
• Advanced daily hands-on proficiency with agentic coding tools including context engineering and prompt management.
• Experience building skills, plugins, and MCP-based workflows adopted by engineers.
• Strong Python and full‑stack capabilities for API and architecture design.

Responsibilities

• Define Gen AI engineering standards across Royal Caribbean Group and lead cross-brand delivery.
• Join GenAI Solutions Engineering and partner with the GenAI CoE.
• Build rapid prototypes with business users for feedback and iteration.
• Establish enterprise practices for context engineering, instruction files, and workflows.
• Design and maintain agent-ready architectures and reusable accelerators.

Skills

Python

Agentic coding tools

Mentoring engineers

Problem solving

Education

Bachelor’s degree in Computer Science or related field

Tools

Claude Code

GitHub Copilot

Cursor

CLAUDEmd

AGENTSmd

MCP servers

GitHub

Azure DevOps

Job description

Lead Gen AI engineering across Royal Caribbean Group by setting standards, building evaluation and delivery frameworks, and guiding teams on agentic coding and LLMOps.

Responsibilities

• Define engineering standards for Gen AI across Royal Caribbean Group and lead delivery of projects spanning multiple brands and business functions
• Join the GenAI Solutions Engineering team within AI & Analytics and partner closely with the GenAI Center of Excellence (CoE)
• Work with business users to understand requirements and rapidly build prototypes for feedback and iteration
• Act as an advanced daily user of agentic coding tools such as Claude Code, GitHub Copilot, Cursor, and similar; establish enterprise practices for context engineering, instruction files (CLAUDEmd/AGENTSmd), plan-then-implement workflows, subagents, parallel sessions, hooks, and permission/sandbox settings
• Design, build, and maintain skills plugins, slash commands, subagents, hooks, and MCP servers that encode standards and workflows (for example scaffolding ticket creation, PR review, test and eval generation) and distribute via a versioned shared plugin catalog
• Publish living, agent-readable engineering standards that remain short, tested, and current as tools and models evolve
• Define an AI-assisted delivery lifecycle: spec-driven development, small reviewable changes, automated AI-assisted code review, and clear human accountability with guardrails for security, secrets, data classification, and IP
• Establish reference architectures, project templates, and reusable accelerators that are agent-ready and provide a secure, tested, production-ready baseline
• Measure impact of AI-assisted engineering (cycle time, rework, defect rates, token cost) and use evidence to refine prompts and practices
• Partner with the GenAI CoE to publish standards, run design reviews, and retire patterns that no longer support enterprise needs
• Set the testing strategy for Gen AI systems, including unit, integration, contract, end-to-end regression, 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 verify test effectiveness
• Build test harnesses for non-deterministic behavior using mocked model/tool responses, recorded fixtures, and deterministic seams around LLM calls
• Create verification loops so agents check their own work (tests, type checks, linters, evals) before human review
• Enforce automated quality gates in CI/CD including tests, static analysis, dependency and secret scanning, AI-assisted review, and evaluation checks so nothing ships without passing gates
• Lead blameless post-incident reviews and feed findings back into standards, skills, and tests
• Own the enterprise evaluation framework for LLM applications and agents: golden datasets, rubric-based and model-graded evals, human review workflows, and metrics for groundedness, hallucination safety, latency, and cost
• Create evaluation approaches for RAG (retrieval precision/recall and 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 thresholds, dashboards, and regression alerts
• Define and validate LLM-as-judge methods, including calibration against human labels and bias checks; guide teams on building representative evaluation datasets
• Govern model prompt and configuration changes through versioned, evidence-based evaluation before promotion
• Lead technical design and delivery of Gen AI initiatives across brands, business functions, and data domains, including cross-domain agents and shared enterprise data and knowledge services
• 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; design agentic workflows using 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; advise when low-code vs custom engineering is the right fit
• Run feasibility studies and proofs of concept, then convert successes into supported production-grade services
• Implement secure authentication and authorization using Microsoft Entra ID, MSAL, and service principals; 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/observability, drift detection, and cost/token monitoring using Azure Monitor and Log Analytics
• Implement AI safety guardrails such as content filtering, prompt injection protection, and output validation aligned with enterprise security, data classification, and responsible AI requirements
• Mentor and coach engineers across teams on working with AI coding agents and raise expectations for design, code quality, testing, and evaluation practice
• Run hands-on enablement (workshops, demos, office hours) to drive adoption of skills and plugins
• Contribute to the AI governance framework to align solutions with enterprise AI principles, compliance needs, and responsible AI practices
• Communicate technical trade-offs, risks, and progress to engineering peers, product owners, and senior leadership

Requirements

• 7+ years of professional software engineering experience
• 2+ years building and shipping Gen AI or LLM-based applications to production
• Bachelor’s degree in Computer Science Engineering (or related field) or equivalent practical experience
• Advanced daily hands-on proficiency with agentic coding tools (for example Claude Code) including context engineering, instruction files, subagents, hooks, and parallel workflows; ability to review and validate AI-generated code
• Demonstrated experience building skills, plugins, slash commands, subagents, or MCP servers adopted by other engineers, distributed via 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); TypeScript or another full-stack language fundamentals for API and architecture design and directing AI agents to maintainable outcomes
• Hands‑on testing expertise covering unit, integration, contract, and end‑to‑end testing (for example pytest), including CI‑based test automation and AI‑assisted test generation/maintenance for non‑deterministic components
• Proven experience designing and operating LLM evaluation: golden datasets, metrics, LLM‑as‑judge, human review, and using evals to gate releases and monitor production quality
• Deep understanding of Gen AI architectures including RAG, tool use, function calling, MCP, and single‑and‑multiple‑agent orchestration
• Proven expertise with Azure services: Azure AI Foundry, Azure Container Apps, Azure AI Search, Azure Databricks, Azure APIM, Azure Key Vault, and Azure Monitor, including 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 2.0, 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 partnering directly with business users to translate requirements into working prototypes across enterprise scale, plus mentoring other engineers
• Excellent problem‑solving skills in a fast‑paced dynamic environment
• Strong written and verbal communication skills for explaining technical trade‑offs and standards to engineers, business partners, and senior leadership

Technologies

• Claude Code, GitHub Copilot, Cursor, CLAUDEmd, AGENTSmd, MCP servers, GitHub
• Azure AI Foundry, Azure Container Apps, Azure AI Search, Azure Databricks
• Python, FastAPI, Nextjs, PostgreSQL
• Model Context Protocol (MCP)
• Microsoft Copilot Studio, Microsoft 365 Copilot
• Microsoft Entra ID, MSAL, Azure Key Vault, Azure APIM, Azure DevOps, Azure Monitor, Log Analytics
• OAuth 2.0, Azure, CI/CD, pytest, TypeScript
• Vector indexing, hybrid search, semantic ranking
• LLM‑as‑judge, LLMOps

Preferred Qualifications

• Master’s degree in Computer Science Engineering, Data Science, or related field
• Experience with the Claude Agent SDK or similar frameworks; running coding agents headlessly in CI/CD for review, testing, or automation
• Experience operating an internal plugin or skills marketplace with versioning, testing, and adoption measurement
• Experience with Databricks (Unity Catalog, Genie, MCP integrations) and enterprise semantic or data layers
• Familiarity with Microsoft Copilot Studio and 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 AI governance or engineering communities of practice in a large enterprise

Location

• Miami, FL (onsite)