Lead AI Engineer - Search, Personalization, Agents (Remote Opportunity - Mexico Based Only)

The Opportunity

Hyatt Hotels Corporation seeks an enthusiastic Lead 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’ll be instrumental in continuing to make Hyatt a leading AIML powered hospitality company and be a part of the 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.

Who We Are

At Hyatt, we believe in the power of belonging and creating a culture of care, where our colleagues become family. Since 1957, our colleagues and our guests have been at the heart of our business and helped Hyatt become one of the best, and fastest growing hospitality brands in the world. Our transformative growth and the addition of new hotels, brands and business lines can open the door for exciting career and growth opportunities to our colleagues.

As we continue to grow, we never lose sight of what’s most important\: People. We turn trips into journeys, encounters into experiences and jobs into careers.

Why Now

This is an exciting time to be at Hyatt. We are growing rapidly and are looking for passionate changemakers to be a part of our journey. The hospitality industry is resilient and continues to offer dynamic opportunities for upward mobility, and Hyatt is no exception.

How We Care for Our People

What sets us apart is our purpose—to care for people so they can be their best. Every business decision is made through the lens of our purpose, and it informs how we have and will continue to support each other as members of the Hyatt family. Our care for our colleagues is the key to our success. We’re proud to have earned a place on Fortune’s prestigious 100 Best Companies to Work For® list since 2013. This recognition is a testament to the tremendous way our Hyatt family continues to come together to care for one another, our commitment to a culture of inclusivity, empathy and respect, and making sure everyone feels like they belong.

We’re proud to offer exceptional corporate benefits which include

• Annual allotment of free hotel stays at Hyatt hotels globally

• Flexible work schedule

• Work-life benefits including wellbeing initiatives such as a complimentary Headspace subscription, and a discount at the on-site fitness center

• A global family assistance policy with paid time off following the birth or adoption of a child as well as financial assistance for adoption

• Paid Time Off, Medical, Dental, Vision, 401K with company match

Who You Are

You are a seasoned and passionate technical contributor with a demonstrable experience in delivering enterprise-grade AI/ML platforms and products with a strong focus on Gen AI capabilities. You thrive in dynamic environments and value experimentation, empathy, and business alignment.

You embody Hyatt’s values of Care, Inclusion, Integrity, Respect, Empathy, and Well-being, and seek opportunities to inspire and develop your team while making measurable business impact.

This is a hands-on, high-impact individual contributor role. The person in this position will serve as a technical leader and thought partner across data science, machine learning engineering, data engineering, platform, product, and business teams. The ideal candidate combines deep applied machine learning engineering expertise with strong product judgment, experimentation rigor, and the ability to translate business problems into scalable AI solutions.

You will be part of a highly visible, collaborative team focused on applying the latest techniques across machine learning, and Generative AI to solve meaningful business problems at enterprise scale.

You will be a part of a ground-floor, hands-on, highly visible team which is positioned for growth and is highly collaborative and passionate about machine learning and AI.

Applying the latest techniques and approaches across the domains of data science, machine learning, and AI isn’t just a nice to have, it’s a must. You will be part of a team passionate about diversity, equity, and inclusion, committed to nurturing curiosity and new skills and building connections with stakeholders, colleagues, and guests across the organization.

The Role

As a Lead 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 technical leadership in machine-learning engineering, system design, observability, agentic application delivery, retrieval systems, and fast, reliable LLM inference.

Search, Personalization, and Retrieval

• Design and build scalable AI systems for natural-language search, semantic retrieval, ranking, recommendations, and personalized guest experiences.

• Develop low-latency retrieval and ranking pipelines that combine keyword search, embeddings, vector retrieval, reranking, business rules, and real-time contextual signals.

• Partner with data scientists to productionize relevance models, recommender systems, and LLM-powered search experiences.

• Define online and offline experimentation approaches, including relevance metrics, latency targets, A/B tests, and business-impact measurement.

Fast LLM Inference and Production Engineering

• Lead the design of high-throughput, low-latency LLM inference services for real-time guest and colleague experiences.

• Optimize serving performance through model selection, quantization, batching, caching, streaming, routing, autoscaling, and efficient GPU utilization.

• Evaluate hosted, open-weight, fine-tuned, and smaller task-specific models based on quality, cost, reliability, privacy, and latency requirements.

• Build production APIs and services for model inference, embeddings, reranking, retrieval, and agent execution.

• Partner with ML engineering and platform teams to deploy systems using AWS-native services and modern containerized infrastructure.

Agentic AI and Multi-Agent Systems

• Architect and implement agentic AI applications that use LLMs, tools, retrieval, workflows, memory, and structured decision-making.

• Build agent-to-agent systems in which specialized agents can delegate work, share relevant context, coordinate on tasks, and produce auditable outcomes.

• Design reliable orchestration patterns for multi-step workflows, including planning, tool execution, state management, retries, handoffs, approvals, and fallback behavior.

• Establish secure agent interfaces for enterprise tools and data, with strong identity, authorization, tenant or property isolation, traceability, and policy enforcement.

• Develop reusable agent platform components, such as tool registries, agent registries, workflow templates, evaluation harnesses, and observability standards.

MLOps, Reliability, and Governance

• Establish robust delivery practices for AI services, including CI/CD, testing, versioning, infrastructure-as-code, reproducibility, and rollback strategies.

• Implement observability for models and agents\: latency, availability, cost, quality, retrieval health, tool failures, drift, safety events, and business outcomes.

• Create evaluation frameworks that include offline benchmarks, human evaluation, adversarial testing, guardrails, bias checks, hallucination analysis, and production monitoring.

• Work with security, privacy, legal, architecture, and governance stakeholders to ensure AI systems are safe, compliant, reliable, and responsibly deployed.

• Support both batch and real-time inference patterns across the full model lifecycle.

Technical Leadership and Collaboration

• Serve as the hands-on technical lead for high-impact AI initiatives from discovery through production operation.

• Translate ambiguous business opportunities into clear engineering problem statements, architecture designs, delivery plans, success metrics, and technical tradeoffs.

• Influence AI and ML platform roadmaps based on business value, technical feasibility, risk, operational cost, and team capacity.

• Mentor engineers and data scientists through design reviews, code reviews, architecture discussions, and engineering best practices.

• Communicate technical decisions, model limitations, risks, and measured outcomes clearly to both technical and business stakeholders.