Senior Machine Learning Engineer – Data Science & Analytics

Largeton Group is seeking a backend-oriented Senior ML Engineer to build and operationalize large-scale production AI/ML services in cloud environments, supporting Hyatt's personalization, analytics, and AI initiatives. Requires strong software engineering fundamentals, cloud experience, and the ability to deliver robust, scalable ML solutions in a dynamic, cross-functional enterprise setting.

Responsibilities:

• Design and implement scalable backend architectures for ML products and services
• Build, deploy, and monitor production-grade ML/AI systems across the product lifecycle (data ingestion, feature engineering, model integration, inference, batch/stream processing)
• Develop and optimize streaming and batch data pipelines; maintain feature stores
• Implement infrastructure-as-code and CI/CD for ML services
• Ensure latency, scalability, and reliability of ML systems
• Support cloud-native ML infrastructure on AWS and GCP
• Collaborate cross-functionally with Data Engineering, Architecture, Governance, and Security teams
• Contribute to system design and technical architecture decisions

Requirements:

• 5+ years of software engineering experience with cloud-native product solutions
• Strong expertise in Python, SQL, PySpark, Docker
• Experience building and maintaining backend systems for ML/algorithmic workloads
• Strong AWS cloud experience; familiarity with GCP
• Track record in designing large-scale streaming and batch data architectures
• Experience with DevOps, CI/CD, and infrastructure-as-code
• Strong analytical, collaboration, and communication skills
• Ability to operate in ambiguous, fast-paced environments and drive end-to-end solutions
• Bachelor's or Master's degree in Computer Science, Software Engineering, or related field (or equivalent experience)
• Experience with SageMaker
• Experience with feature stores
• Experience with real-time inference
• Experience with hospitality or personalization systems
• Experience with LLM-enabled apps