Machine Learning Engineer

The ML Engineer is responsible for architecting the productionalized solution around rules-based and AI/ML models to integrate predictions seamlessly into the business processes, ensuring governance, resiliency, explainability, reproducibility, and scalability of the models. We are looking for a highly capable ML Platform Engineer to optimize rules-based and machine learning systems. As an engineer for the ML platform you will be working at the intersection of machine learning, DevOps, and data engineering (i.e. MLOps). 

ML Platform Engineer Responsibilities:

• Lead and consult with business stakeholders and data science teams to define data engineering and MLOps requirements.
• Transforming business and data science prototypes and applying appropriate algorithms and tools.
• Solving complex problems with multi-layered data sets, as well as optimizing existing machine learning libraries and frameworks.
• Developing reusable data and feature stores for rules-based and AI/ML models.
• Developing 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.
• Build simple front-end reporting solution for shipboard engineers to consume the insights. (web app development)

  Required Skills  

• Experience building scalable machine learning systems and data-driven products working with cross-functional teams.
• Well-developed software engineering fundamentals, including use of proper development, QA, and production environments, and the ability to write production-level code when needed.
• Experience creating a python package
• Proficiency in Python and experience with common data analytics packages (e.g. Numpy, Pandas, Sklearn, PySpark).
• Proficiency in Databricks & MLFlow
• Proficiency in SQL.
• Good communication skills and the ability to understand and synthesize requirements across multiple project domains.
• Works effectively with cross-functional teams