Interested in this AI/ML Engineer role at Preferred Travel Group?
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About This Role
Who we are:
At Preferred Travel Group, we champion the power of travel to inspire meaningful connections and enrich lives around the world. As a global family of brands and programs representing the finest independent hotels and resorts, we are united by a shared belief in authenticity, collaboration, and the value of independent spirit. Our culture reflects our ideology in action: people first, relationships at the center, and a commitment to creating lasting impact for our partners, our global community, and one another. When you join Preferred, you become part of a purpose\-driven organization where ideas are welcomed, growth is encouraged, and your work contributes to shaping the future of travel.
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Why this role matters:
We are looking for a visionary, data\-driven AI and product leader to help shape the future of digital experiences across Preferred Travel Group's global portfolio of independent luxury hotels. At PTG, the Digital Product organization is moving beyond traditional booking engines to create personalized, frictionless guest experiences that drive engagement, loyalty, and revenue. The Director of AI \& Data Product will lead PTG's AI and data product strategy, owning the AI roadmap for customer\-facing digital experiences, driving Generative Engine Optimization initiatives, and delivering AI\-enabled conversational experiences that better understand customer intent, remember user preferences, and enhance personalization. Working closely with IT, Data, Engineering, and AI teams, this leader will help define the future of PTG's data platform architecture, governance, and activation strategy while ensuring that data and AI capabilities directly support business growth and customer value.
Success in this role comes from delivering AI\-powered digital experiences that increase engagement, personalization, and conversion across PTG's owned channels. The Director will establish a clear and scalable AI and data product roadmap, ensuring data and AI capabilities are effectively leveraged to enhance customer experiences, drive revenue growth, and support strategic business objectives
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What you’ll deliver:
- Generative Engine Optimization (GEO) \& Search Defense
+ Architect and execute PTG's GEO strategy, partnering with the Product Managers, stakeholders and IT resources. Drive LLM visibility for our hotels, offers, and loyalty program within the major LLMs.
+ Partner with the Content team to structure data specifically for AI ingestion and RAG architectures, including schema markup, structured content models, and entity disambiguation across the hotel portfolio.
+ Define and track GEO performance metrics, establishing PTG's methodology for measuring AI\-driven discoverability.
- Commercial Data Platform \& CDP Ownership
+ Own the product roadmap for PTG's customer data foundation and data as a product strategy, including understanding of a medallion architecture integrating booking, loyalty, clickstream, and marketing data.
+ Serve as the product owner of PTG's Customer Data Platform (CDP), overseeing the unified member profile layer that consolidates identity resolution, behavioral signals, and loyalty data into actionable segments for marketing, personalization, and AI model grounding.
+ Define the data products with key stakeholders including Marketing and IT for gold\-layer outputs that serve downstream consumers: campaign activation, revenue analytics, loyalty operations, and AI/ML feature pipelines.
+ Support data governance and privacy compliance for the commercial platform, including DPIA maintenance, PII handling standards, and vendor data processing agreements, prioritizing the needs of GDPR, CCPA, and hospitality\-sector obligations.
+ Partner closely with IT, Data, Engineering, and AI teams to maintain the architectural boundary between the data product and IT infrastructure, while aligning platform design, governance, and downstream activation needs across each environment.
- AI Assistant Product Ownership
+ Own the roadmap and deployment of PTG's AI chat interfaces and assistants across web and mobile, for both usability and structured zero\-party data collection.
+ Integrate conversational interfaces with the loyalty platform and booking stack, enabling intent\-driven recommendations, personalization and seamless booking.
- Operational AI \& Automation
+ Identify and build internal AI tools that eliminate heavy manual operational processes across content, onboarding, loyalty operations, and member support.
+ Oversee the ongoing optimization content ingestion automation to dynamically update hotel content in Preferred’s owned channels.
+ Partner with I Prefer Member Services to deploy AI\-driven deflection tools, measuring deflection rate, member satisfaction, and cost\-per\-contact impact.
- Innovation \& Vendor Strategy
+ Lead organization insights on emerging AI technologies, LLM capabilities for AI in hospitality and loyalty, as they impact the digital journey.
+ Vet and/or manage relationships with AI and data vendors, including CDP, analytics, and AI platform partners, compliance with PTG's data governance requirements.
+ Prototype and pitch net\-new AI capabilities that drive incremental revenue, operational savings, or meaningful improvements in guest personalization and loyalty engagement.
What you’ll bring to the team:
- 8\+ years of experience in technical product management, with at least 2 years specifically focused on AI, machine learning, NLP, conversational UI, or data platform products.
- Practical understanding of Large Language Models, RAG architectures, prompt engineering, and how AI systems index, retrieve, and cite web and structured data.
- Hands\-on product ownership experience with a commercial data platform or data warehouse environment, defining data products, working with dbt or similar transformation tooling, and managing the relationship between raw data ingestion and downstream analytics or AI consumers.
- Demonstrated experience with Customer Data Platforms (CDPs), understanding of identity resolution, unified profiles, segmentation, and how CDP outputs connect to personalization and campaign activation.
- Proven experience building and scaling consumer\-facing AI products or advanced automation tools within an environment.
- Strong architectural understanding of modern data and AI stacks sufficient to guide engineering teams, including customer data foundations, event streaming, API\-first integrations, and ML feature pipelines.
- Demonstrated ability to translate complex AI and data concepts into clear business value, ROI, operational savings, conversion lift, and loyalty engagement impact for non\-technical executive audiences.
- Comfort operating in ambiguity with an entrepreneurial mindset: prototyping rapidly, defining metrics where none exist, and pivoting based on data.
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What Would Make You Stand Out:
- Familiarity with data privacy regulations (GDPR, CCPA) and experience maintaining DPIAs or equivalent compliance documentation for data\-intensive platforms.
- Background working with clickstream data, behavioral analytics, and CDP vendor ecosystems (Amperity, Segment, mParticle, or equivalent).
- Experience with GEO, AEO, or structured data strategy for AI discoverability, including schema markup, entity\-based content models, or LLM citation optimization.
- Familiarity with headless CMS platforms and how content architecture decisions affect both AI ingestion and front\-end delivery.
Where you’ll thrive:
- You are comfortable operating in a rapidly evolving technology landscape, thrive on continuous learning, and bring a proactive approach to identifying opportunities for automation and efficiency.
- You enjoy leading multiple priorities in a fast\-paced environment, balancing strategic thinking with disciplined execution.
- You are confident making well\-informed decisions, taking calculated risks, and driving results with a strong sense of ownership and accountability.
- You communicate with clarity and conviction, working independently while maintaining strong follow\-through and attention to detail when coordinating with others.
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Our Working Culture:
With our in\-office philosophy, our associates are expected to be in the office at least three days per week, supporting a healthy balance between in\-person collaboration and flexible remote work. We take pride in our vibrant and inclusive culture, which thrives on meaningful connection, shared purpose, and cross\-functional teamwork. In\-office engagement plays a vital role in fostering spontaneous collaboration, accelerating innovation, and strengthening relationships across teams. It also provides valuable opportunities for mentorship, professional development, and a deeper sense of community.
Please note: While the current expectation is a minimum of three days per week in the office, this may evolve over time in alignment with business needs and our continued commitment to culture\-building
Disclaimer \- This description reflects the general nature and level of the role. It is not intended to be an exhaustive list of responsibilities or requirements
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Salary:
$155,000 \- $175,000, actual compensation within this range will be determined by multiple factors including candidate experience and expertise.
Salary Context
This $155K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Preferred Travel Group, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($165K) sits 23% below the category median. Disclosed range: $155K to $175K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Preferred Travel Group AI Hiring
Preferred Travel Group has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span New York, NY, US, US. Compensation range: $100K - $175K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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