Lead, Machine Learning Operations Engineer

Royal Caribbean Cruises - Miami, FL

Full-time - Mid Level

Miami, FL

1,001-5,000 employees

Administrative and Support Services

About the position

The Lead Machine Learning Operations Engineer at Royal Caribbean Group is responsible for overseeing the implementation and iteration of AI/ML solutions within the organization. This role involves providing architectural guidance, developing reusable frameworks, optimizing code, and ensuring the scalability of ML solutions. The engineer will collaborate closely with data scientists, data engineers, and business stakeholders to drive new AI capabilities and support various data science projects, including the establishment of a Center of Excellence for GenAI use cases.

Responsibilities

• Drive new AI capabilities for the ML Platform including GenAI, OpenAI, DBRX, and Mosaic ML.
• Create a Center of Excellence to govern GenAI use cases, providing guidance and establishing best practices.
• Provide support and guidance to multiple Data Science projects.
• Build, maintain, and document reusable frameworks by enhancing existing Python packages and creating new ones.
• Automate CI/CD testing and deployments incorporating MLOps best practices.
• Implement monitoring capabilities for model performance and effectiveness in production.
• Maintain and enhance the internal Azure OpenAI website used across the company.

Requirements

• Master's or PhD degree in computer science, data science, mathematics, or a related field.
• 7+ years of overall experience in Data Analytics & AI.
• 5+ years of experience with ML Engineering and/or ML Ops.
• Proficient in Python and experience with common data analytics packages (e.g., Numpy, Pandas, Sklearn, PySpark).
• Proficient in SQL.
• Experience creating and maintaining Python packages.
• Experience with web development frameworks and languages (JavaScript, ReactJS, Flask, or others).
• Familiarity with frameworks and languages designed for big-data analytics including Spark, Databricks, and Azure Data Factory.
• Experience with MLOps and ML experiment tracking tools such as Azure DevOps and MLFlow or similar.
• Experience with cloud computing services such as Microsoft Azure, Amazon Web Services, and/or Google Cloud Platform.

Nice-to-haves

• Familiarity with different data science techniques: statistics, machine learning, or cognitive AI.
• Experience serving containerized solutions in the cloud.

Benefits

• Competitive compensation package
• Career development opportunities
• Health insurance
• Paid time off
• Flexible scheduling