Lead AI Engineer – Search, Personalization, Agents

Hyatt

. Build and operate production AI systems improving Hyatt’s search, personalization, guest experiences, colleague productivity, and operational workflows .

Posted 9/25/2026full-timeRemote • MexicoSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and operating production AI systems, particularly in natural-language processing, LLM applications, and scalable data pipelines. Proficient in deploying AI solutions using AWS services and implementing observability and governance practices.

Highest-signal resume keywords

Machine Learning ExpertiseNatural Language Processing (NLP)AWS Cloud ServicesPython ProgrammingGenerative AI Solutions

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningNatural Language ProcessingRecommender SystemsData Pipeline DesignReal-Time InferenceModel Lifecycle ManagementCI/CD PracticesObservability ToolsSQLPySpark

Soft Skills

Interpersonal SkillsCommunication Skills

Tools & Technologies

AWS SageMakerAWS ECS/EKSAWS Step FunctionsAWS LambdaAWS GlueTensorRT-LLMVLLMSGLangHF OptimumONNX Runtimes

Certifications & Qualifications

Master's Degree in Computer SciencePh.D. Preferred

Industry Keywords

AI SystemsLLM ApplicationsGenerative AIResponsible AI PracticesAgile Development

Tech Stack

Tools & technologies

AWSCloudDockerPySparkPythonSQL

About the role

Key responsibilities & impact

Build and operate production AI systems improving Hyatt’s search, personalization, guest experiences, colleague productivity, and operational workflows
Design scalable AI systems for natural-language search, semantic retrieval, ranking, recommendations, and personalized guest experiences
Develop low-latency retrieval and ranking pipelines using 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 experimentation approaches, relevance metrics, latency targets, A/B tests, and business-impact measurement
Lead high-throughput, low-latency LLM inference services
Optimize model serving through model selection, quantization, batching, caching, streaming, routing, autoscaling, and efficient GPU utilization
Build production APIs and services for inference, embeddings, reranking, retrieval, and agent execution
Deploy systems using AWS-native services and modern containerized infrastructure
Architect and implement agentic AI and multi-agent applications using LLMs, tools, retrieval, workflows, memory, and structured decision-making
Design orchestration patterns for planning, tool execution, state management, retries, handoffs, approvals, and fallback behavior
Establish secure agent interfaces with identity, authorization, isolation, traceability, and policy enforcement
Develop reusable agent platform components, registries, workflow templates, evaluation harnesses, and observability standards
Establish CI/CD, testing, versioning, infrastructure-as-code, reproducibility, and rollback practices
Implement observability for models and agents, including latency, availability, cost, quality, retrieval health, failures, drift, safety events, and business outcomes
Create evaluation frameworks covering benchmarks, human evaluation, adversarial testing, guardrails, bias checks, hallucination analysis, and production monitoring
Work with security, privacy, legal, architecture, and governance stakeholders to ensure safe, compliant, reliable, and responsible AI deployment
Support batch and real-time inference across the model lifecycle
Serve as hands-on technical lead for AI initiatives from discovery through production operation
Translate business opportunities into engineering problem statements, architectures, delivery plans, success metrics, and technical tradeoffs
Influence AI and ML platform roadmaps
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 to technical and business stakeholders

Requirements

What you’ll need

Master’s degree in computer science, Software Engineering, or related field
6+ years of experience in machine learning roles focused on NLP/NLU, recommender systems, or LLM applications
4+ years of experience deploying LLMs or other Generative AI solutions to production
Expertise in AWS cloud services, including SageMaker, ECS/EKS, Step Functions, Lambda, and Glue
Expertise in TensorRT-LLM, vLLM, SGLang, HF Optimum, and ONNX runtimes
Strong programming skills in Python
Experience with SQL, PySpark, and containerization such as Docker
6+ years of experience designing scalable data pipelines and ML systems for real-time and batch inference
Familiarity with ML observability and governance tools
Solid understanding of responsible AI practices, CI/CD pipelines, Agile development practices, and model lifecycle management
Excellent interpersonal and communication skills
Ph.D. preferred

Benefits

Comp & perks

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
Discount at the on-site fitness center
Global family assistance policy with paid time off following the birth or adoption of a child
Financial assistance for adoption
Paid Time Off
Medical insurance
Dental insurance
Vision insurance
401K with company match