A Generative Artificial Intelligence Engineer develops, designs, and manages generative models and algorithms that support the generation of new content in the form of images, text, audio, and other multimedia. They utilize GPTs, GANs, VAEs, and other deep learning architectures to craft systems capable of generating data. May work with data scientists, machine learning engineers, and software developers.
WORK FOR A WINNING TEAM THAT NOW OFFERS BENEFITS FROM DAY ONE
At Hilton Grand Vacations, you will become a part of a culture that encourages and motivates you toward achieving your goals. Here's why you will love it here: Recognition Programs and Rewards Excellent health care options, including medical, dental, and vision A people-first culture
Go Hilton:
Travel Discounts Program Hilton hotel rates worldwide.
Perks at work:
Employee Pricing platform Employee Assistance Program that supports your physical and mental well-being. Paid Vacation Time and Paid Sick Days 401(k) program with company match Tuition reimbursement programs Numerous learning and advancement opportunities And more! What Will I be Doing? The Lead Data Scientist, AI Engineering sets technical direction for advanced analytics, machine learning, and applied AI that improve business performance and member/guest experience at Hilton Grand Vacations. This role designs and delivers analytical products and AI-powered solutions—from classical prediction and recommender systems to LLM-orchestrated pipelines, agents, and generative experiences—on Databricks and the broader Azure stack. Primary domains include Inventory Management, Sales Efficiency, Marketing, Member/Guest Satisfaction, Operational Reporting, and Digital Analytics, with extensions into Pricing, Portfolio performance, and personalized member/guest experiences. The Lead partners with MLOps, Operational Reporting, IT, and product stakeholders to define scalable patterns for model and AI application lifecycle management, governs quality and risk for production assets, and elevates delivery quality across the analytics community Define and prioritize an analytics, ML, and AI engineering roadmap; translate business strategy into a portfolio of models, experiments, LLM applications, and analytical products with clear success metrics. Lead end-to-end delivery of complex ML, statistical, and generative AI solutions—from problem framing and data strategy through deployment, monitoring, evaluation, and iteration—in partnership with MLOps and engineering. Build and evolve an extensible data science and AI platform on Databricks (Python, SQL, Databricks ML, Model Serving, Asset Bundles) integrated with Azure OpenAI and related services. Design and ship applied AI systems relevant to IMA work: prompt and context engineering, LLM orchestration pipelines (e.g. KPI → narrative → media/HTML), retrieval-augmented generation (RAG) where appropriate, evaluation harnesses, guardrails, and Copilot/agent workflows that automate operational processes. Own architecture and standards for forecasting, recommender/personalization systems, predictive analytics, and production AI apps; ensure reproducibility, documentation, CI/CD readiness, and operational excellence. Identify revenue and efficiency opportunities using advanced analytics and AI; initiate projects that bridge operational metrics to financial and customer outcomes. Provide a feature store, reusable components, and technical support that accelerate other analytics teams. Mentor and coach data scientists and analysts through design reviews, code reviews, and pairing—raising the bar on experimentation, responsible AI, and production quality—without formal supervisory responsibility. Respond quickly to high-impact ad-hoc requests; deliver actionable insights and clear recommendations to leadership. Perform prediction modeling, customer lifetime value, and related analyses that inform strategy.
Ensure MLOps / AIOps discipline:
maintain production code and assets, monitoring, retraining/refresh patterns, incident response, and reliability for models and generative pipelines. Represent IMA analytics with senior business and technology stakeholders: set expectations, manage trade-offs, and communicate implications for operations and financial outcomes. Participate in data governance—metrics definitions, data quality, and source improvement—and formulate UX strategies so analytical and AI outputs are adopted by end users. Perform additional project-based and ad-hoc analysis as assigned.
