Senior Machine Learning / MLOps Engineer (Remote Opportunity)

Hyatt Hotels Corporation seeks an enthusiastic Senior ML Engineer to join our Data Science and Machine Learning department. In this role, you will be collaborating closely with the broader Data and Analytics team, where you’ll be instrumental in continuing to make Hyatt a leading hospitality company.

Responsibilities:

• Design and implement end-to-end ML systems, including data ingestion, feature processing, model training, and model serving
• Architect and deploy scalable AI services supporting real-time and batch inference use cases
• Build and maintain ML infrastructure across cloud environments (e.g., EC2, EKS, SageMaker, specialized inference hardware)
• Develop and evolve MLOps platforms, including training pipelines, deployment workflows, feature stores, and model observability
• Implement CI/CD and infrastructure-as-code patterns to automate model lifecycle management
• Optimize model training and inference performance for cost, latency, and hardware efficiency
• Monitor production ML systems for accuracy, reliability, and operational health
• Partner cross-functionally with data engineering, architecture, governance, and security teams to ensure compliant and scalable solutions
• Mentor team members on ML engineering, system design, and operational best practices
• Contribute to special initiatives that advance AI platform maturity and engineering standards

Requirements:

• Master's degree in Computer Science, Software Engineering, Machine Learning, or a related field
• 5+ years of experience building and operating machine learning solutions in cloud environments, with focus on AI services and MLOps foundations
• Demonstrated hands-on experience delivering end-to-end ML systems, spanning model development, deployment, and production infrastructure
• Proficiency with modern ML engineering tooling, including cloud platforms, data pipelines, and CI/CD workflows
• Experience designing and scaling real-time and batch inference systems in production
• Hands-on experience with deep learning frameworks and model optimization for performance and cost
• Experience building or contributing to shared MLOps platforms, feature stores, or ML observability solutions
• Familiarity with cloud security, governance, and compliance standards