Royal Caribbean Cruises - Miami, FL

posted 2 months ago

Full-time
Miami, FL
Administrative and Support Services

About the position

The Machine Learning Operations Engineer at Royal Caribbean Group is a pivotal role that combines machine learning, DevOps, and data engineering to create production-ready solutions for AI/ML models. This position is based in Miami, Florida, and reports directly to the Manager of Machine Learning Engineering. The primary responsibility of the Machine Learning Engineer is to architect and implement solutions that integrate predictions from machine learning models into business processes. This includes ensuring that the models are governed, resilient, explainable, reproducible, and scalable. The role requires collaboration with business stakeholders and data science teams to define the necessary data engineering and MLOps requirements, ensuring that the solutions meet the needs of the organization. In this role, you will lead the development and maintenance of machine learning frameworks, specifically Python packages that will be utilized across various projects. You will also be responsible for creating reusable data and feature stores for both rules-based and AI/ML models, as well as developing alerting tools to monitor the performance and effectiveness of productionized models. Automation of deployments is a key aspect of this position, as you will incorporate MLOps best practices into the solutions you develop. Additionally, thorough documentation of frameworks and machine learning processes is essential to ensure clarity and reproducibility. The ideal candidate will possess a strong background in software engineering, data analytics, and machine learning, with a proven track record of building scalable machine learning systems and data-driven products. Excellent communication skills are necessary to effectively synthesize requirements across multiple project domains, making this a collaborative and dynamic role within the organization.

Responsibilities

  • Lead and consult with business stakeholders and data science teams to define data engineering and MLOps requirements.
  • Build and maintain machine learning frameworks (python packages) used across multiple projects.
  • Develop reusable data and feature stores for rules-based and AI/ML models.
  • Develop alerting tool frameworks for monitoring productionized model performance and effectiveness.
  • Automate deployments incorporating MLOps best practices into productionalized solutions.
  • Document frameworks and machine-learning processes.

Requirements

  • Bachelor's degree in software engineering, computer science, data science, mathematics, or a related field.
  • 5+ years of overall experience in Data Analytics.
  • 2+ years of experience with ML Engineering and/or ML Ops.
  • Experience building scalable machine learning systems and data-driven products working with cross-functional teams.
  • Experience creating python packages.
  • Well-developed software engineering fundamentals, including use of proper development, QA, and production environments, and the ability to write production-level code when needed.
  • Proficiency in Python and experience with common data analytics packages (e.g. Numpy, Pandas, Sklearn, PySpark).
  • Proficiency in SQL.
  • Good communication skills and the ability to understand and synthesize requirements across multiple project domains.

Nice-to-haves

  • Master's or PhD degree in computer science, data science, mathematics, or a related field.
  • Experience with Agile Software Development.
  • Familiarity with frameworks and languages designed for big-data analytics, including Spark and Azure Data Factory.
  • Experience with MLOps and ML experiment tracking tools, such as Azure DevOps and MLFlow or similar.
  • Experience with cloud computing frameworks or APIs, such as Microsoft Azure, Amazon Web Services and/or Google Cloud Platform.
  • Familiarity with different data science techniques: statistics, machine learning, or cognitive AI.

Benefits

  • Competitive compensation package
  • Career development opportunities
  • Unique ways to explore the world
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