Senior Machine Learning Engineer, Data Science & Analytics

This is a hands-on, in-the-weeds backend software engineering role, not a Data Science position. The hiring manager has been explicit on this point: candidates with Data Science, research, or analytics-only backgrounds are not a fit, regardless of how strong their ML knowledge is. This role requires someone who writes production backend code daily, builds and ships scalable systems, and owns infrastructure end-to-end. Please screen accordingly before submitting.
Position Overview
Hyatt is seeking a Senior Machine Learning Engineer to join its Data Science & Analytics organization. This is a backend engineering role centered on building and operationalizing scalable AI/ML systems in production - hands-on software development, not model research or data analysis.
The engineer will work closely with Data Scientists, Data Engineers, and Architecture teams to productionize ML solutions powering personalization, recommendation systems, analytics platforms, chatbot interfaces, and operational intelligence applications across Hyatt's digital ecosystem. The environment supports roughly 20 active applications and services, and candidates must be comfortable operating with ambiguity and driving solutions independently.
What the Hiring Manager Is Prioritizing
Foundational backend engineering strength, adaptability, and critical thinking matter more than exact tool matches. Strong candidates will demonstrate:
Exceptional software engineering and computer science fundamentals
Hands-on experience building and shipping scalable backend systems supporting ML workloads
Ability to architect, deploy, and maintain production-grade AI/ML services
Comfort working in ambiguous, fast-evolving environments
Strong analytical and systematic problem-solving skills
Fast learning ability and intellectual curiosity
Experience collaborating cross-functionally with Data Scientists and Engineers
Proven delivery experience in enterprise or high-scale technology environments
Candidates with primarily research-heavy, analytics-only, or pure Data Science backgrounds will not be considered, unless they also bring substantial, demonstrable production backend engineering experience. This is a builder/engineer role first.
Core Responsibilities
Design and implement scalable backend architectures supporting ML products
Build and operationalize AI/ML services across the full product lifecycle: data ingestion, feature engineering, model integration, real-time inference, batch processing, deployment, and monitoring
Partner with Data Scientists to productionize ML models (engineering the pipeline, not building the models)
Develop streaming and batch data processing workflows at scale
Implement infrastructure-as-code and CI/CD deployment pipelines
Enhance and maintain feature store workflows and ML data pipelines
Optimize latency, scalability, and reliability of ML systems
Build services supporting personalization, recommendation engines, search, analytics, and conversational AI
Collaborate with Data Engineering, Architecture, Governance, and Security teams
Support cloud-native ML infrastructure within AWS and GCP
Contribute to system design discussions and architecture decisions
Required Qualifications
5+ years of hands-on software engineering experience implementing cloud-native solutions
Strong, demonstrable experience building backend systems supporting ML/algorithmic products
Proficiency in Python, SQL, PySpark, Docker
Strong AWS experience; experience with GCP
Experience building streaming and batch data architectures at scale
Strong system design and backend architecture experience
Experience in Agile environments with DevOps/CI-CD practices
Excellent communication and collaboration skills
Master's degree in Computer Science, Software Engineering, or related field (Bachelor's + strong equivalent experience acceptable)
Preferred Qualifications
Experience with SageMaker
Understanding of feature stores
Hospitality, personalization, or recommendation system experience
Real-time ML inference systems
Infrastructure-as-code experience
Experience supporting AI/LLM-enabled applications (team uses existing LLMs rather than building foundational models)