The Opportunity
Hyatt Hotels Corporation seeks an enthusiastic Senior AI Engineer to join our AIML Team. In this role, you will be collaborating closely with our partners across ML Engineering, Data Engineering, Platform, Product, and Finance teams. You will be part of a team that is passionate about our purpose, committed to nurturing curiosity and new skills, and building connections across the organization with colleagues, customers, and guests.
The Role
As a Senior AI Engineer, you will build and operate production AI systems that improve Hyatt's search, personalization, guest experiences, colleague productivity, and operational workflows. This is a hands-on individual-contributor role focused on machine-learning engineering, system design, observability, agentic application delivery, retrieval systems, and fast, reliable LLM inference.
Generative AI and Machine Learning Engineering
• Design, prototype, and productionize Generative AI solutions in NL Search, Information Retrieval and Recommender Systems.
• Build and evaluate LLM-powered applications, including retrieval-augmented generation, prompt engineering, fine-tuning, embeddings, semantic search, and agentic or workflow-based AI systems.
• Develop robust model evaluation frameworks, including offline metrics, human evaluation, guardrail testing, bias and safety checks, and business-impact measurement.
• Identify opportunities to apply AI to improve guest experiences, colleague productivity, operational efficiency, and commercial outcomes.
• Translate ambiguous business problems into clear data science problem statements, solution designs, success metrics, and implementation plans.
Technical Expertise as an Individual Contributor
• Serve as a hands-on technical resource for high-impact AI and machine learning initiatives.
• Lead solution design, modeling decisions, experimentation strategy, and technical tradeoff discussions.
• Partner with ML engineering and data engineering teams to deploy scalable real-time inference pipelines and batch processing workflows.
• Influence technical roadmaps and help sequence data science initiatives based on business value, feasibility, risk, and team capacity.
• Partner with ML scientists and ML practitioners through design reviews, code reviews, ML Engineering best practices, and knowledge sharing.
Production AI, MLOps, and Cloud Delivery
• Collaborate with ML engineering to productionize models and AI services using AWS-native tools and modern MLOps practices.
• Contribute to scalable ML system design, including data pipelines, feature workflows, model serving, observability, monitoring, and lifecycle management.
• Apply strong software engineering practices, including version control, CI/CD, testing, reproducibility, containerization, and documentation.
• Support deployment patterns for both batch and low-latency inference use cases.
Cross-Functional Collaboration
• Work closely with product owners, ML scientists, ML engineers, data engineers, architects, and business stakeholders to deliver end-to-end algorithmic products.
• Communicate model behavior, limitations, assumptions, risks, and business impact clearly to technical and non-technical audiences.
• Define measurable success criteria and help evaluate whether AI solutions are delivering intended outcomes.
• Champion responsible AI, inclusive design, and practical experimentation across projects.
