ML Engineer training diffusion models for Hapiko’s voice - activated Stickerbox, which turns kids’ spoken ideas into stickers. Bringing Spin Master characters into production safely and on - model.
About the role
- Senior ML Engineer building production AI services and MLOps platforms for Hyatt, a global hospitality company. Optimizing cloud-based inference, infrastructure, and model operations.
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, including EC2, EKS, SageMaker, and 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 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 initiatives advancing 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, focused on AI services and MLOps foundations
- 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
- Must be legally authorized to work in Canada at the time of application and throughout employment
- Proof of eligibility to work in Canada required
Benefits
- Annual allotment of free hotel stays at Hyatt hotels globally
- Flexible work schedule
- Global family assistance policy with paid time off following the birth or adoption of a child
- Financial assistance for adoption
- Extended Health Benefits for you and your dependents
- Paid medical days
- Employer RRSP Matching Contributions
- Fitness and Wellness Allowance
- Cell Phone Allowance
- Annual bonus eligibility
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