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About This Role
Holland America Line has been exploring the world since 1873. Our ships offer innovative features and enriching experiences focused on destination exploration and personalized travel, inviting guests to savor the journey.
The Manager, AI Innovation is responsible for advancing Holland America Line and Seabourn's AI innovation capability through hands-on solution development, strategic portfolio support, enterprise enablement, and scalable operating practices. This role leads a team of Analysts through complex AI discovery and prototyping efforts, translates ambiguous business problems into actionable AI use cases, and builds practical tools, workflows, prompt assets, automations, and decision-support solutions that help teams improve productivity, decision quality, guest experience, and commercial outcomes. In addition to developing AI-enabled solutions, the Manager serves as a force multiplier for the Director, AI Innovation by helping shape the AI roadmap, preparing leadership-ready recommendations, structuring the use case portfolio, researching emerging AI trends, and converting strategy into repeatable processes, standards, content, and ways of working. The role partners closely with IT, data, security, privacy, architecture, business operations, and functional leaders to define how AI ideas move from intake and experimentation through review, production evaluation, adoption, and ongoing support. The position requires strong business judgment, technical fluency, executive-level communication, and the ability to influence without direct authority across a matrixed organization.
Here’s a summary of what Holland America Line is looking for. Is this you?
Responsibilities:
Advanced AI Solution Discovery, Design & POC Delivery
- Lead discovery for complex AI opportunities across multiple business areas, including facilitation of stakeholder interviews, workflow mapping, pain point analysis, success metric definition, and readiness assessment.
- Translate ambiguous business challenges into clear AI use cases, requirements, user stories, acceptance criteria, risks, assumptions, dependencies, and business value hypotheses.
- Build functional AI-enabled prototypes and tools, including agents, copilots, prompt libraries, workflow automations, dashboards, knowledge assistants, decision-support tools, and lightweight applications that business users can test and evaluate.
- Design prototypes with scalability and production evaluation in mind by documenting data needs, process impacts, user roles, controls, technical assumptions, limitations, and support requirements.
- Lead user testing and iteration cycles, synthesize feedback, evaluate whether solutions meet business objectives, and make recommendations on whether to scale, refine, pause, or retire use cases.
Enterprise AI Capability, Trend Intelligence & Advisory
- Continuously monitor emerging AI capabilities, vendor tools, generative AI patterns, agentic workflows, automation trends, responsible AI practices, and relevant developments in cruise, travel, hospitality, marketing, revenue, contact center, guest experience, onboard, and corporate operations.
- Translate external AI trends into practical internal implications by identifying what is actionable now, what should be piloted, what should be monitored, and what should be avoided due to risk, immaturity, cost, or limited business value.
- Develop guidance, examples, demos, playbooks, use case libraries, maturity models, evaluation rubrics, prompt frameworks, and decision aids that help leaders and teams understand how to apply AI responsibly.
- Act as an internal advisor to business stakeholders by helping them distinguish between automation, analytics, generative AI, workflow redesign, and process improvement opportunities.
- Build organizational AI fluency by making complex technical concepts understandable, actionable, and relevant to day-to-day business work.
IT Partnership, Governance & Scalable Ways of Working
- Partner with IT, data, security, privacy, architecture, product, and Technical Automation teams to define the end-to-end operating model for AI experimentation, review, handoff, production evaluation, deployment readiness, and support.
- Partner with PMO to support repeatable processes for AI intake, use case qualification, risk review, data assessment, solution documentation, testing, user acceptance, escalation, and transition to technical delivery teams.
- Translate business needs into technical context for IT partners and translate technical, security, privacy, and architecture constraints into clear business guidance and decisions.
- Identify reusable technical and operating patterns, including approved tools, common data requirements, prompt and knowledge-management practices, control points, documentation standards, and production-readiness checklists.
- Help reduce friction between business experimentation and enterprise technology delivery by ensuring prototypes are documented, tested, governed, and positioned for scalable implementation where appropriate.
Content Strategy, Enablement Assets & Business Adoption
- Own the substance of AI enablement content by defining what information, examples, use cases, guidance, recommendations, and messages should be shared with business audiences to drive understanding and adoption.
- Partner with L&D to create clear, polished content such as training materials, FAQs, playbooks, demo scripts, prompt guides, responsible-use guidance, adoption toolkits, leadership updates, and audience-specific communications.
- Partner with business operations, communications, change management, and functional leaders on distribution and activation plans while ensuring the underlying AI content is accurate, practical, timely, and aligned to strategy.
- Facilitate workshops, demos, office hours, discovery sessions, and working sessions that help teams identify opportunities, understand AI capabilities and limitations, and adopt new tools or processes.
- Gather adoption feedback and business questions, identify recurring education needs, and convert insights into improved content, tools, processes, and enablement priorities.
Team Capability Building, Standards & Operational Excellence
- Help mature the AI Innovation team's operating model by creating templates, knowledge repositories, intake forms, evaluation criteria, prototype documentation standards, reporting routines, and metrics for tracking value and adoption.
- Provide expert guidance and quality review for use case framing, prototype design, requirements documentation, stakeholder materials, business cases, and adoption content.
- Identify opportunities to improve how the team works, including prioritization, collaboration routines, status reporting, tool selection, handoff practices, and knowledge-sharing mechanisms.
- Model senior individual contributor leadership by proactively identifying gaps, solving operational problems, coaching others informally, and raising the quality and consistency of AI Innovation deliverables.
Knowledge, Skills & Abilities:
- Scope: The Manager, AI Innovation operates with broad cross-functional scope across AI strategy, solution development, portfolio enablement, content creation, and operating-model design. The role is accountable for independently leading complex, ambiguous work that spans business stakeholders, IT partners, governance functions, and adoption audiences. Unlike a role focused only on individual prototypes, this position is responsible for building reusable capabilities, processes, content, and standards that allow the AI Innovation function and the broader organization to scale AI more consistently and responsibly.
- Problem Solving: The position requires advanced problem solving in a fast-changing and often undefined AI environment. The Manager must diagnose business problems, determine whether AI is the right solution path, structure experiments, evaluate tradeoffs, size potential value, identify risks, and recommend practical paths forward with incomplete information. The role must balance speed of experimentation with governance, security, privacy, scalability, user adoption, and business impact. The Manager is expected to simplify complex AI concepts for non-technical audiences while maintaining enough technical depth to produce useful prototypes and credible recommendations.
- Impact: This role has impact beyond individual project delivery. The Manager improves the organization's ability to identify, evaluate, build, govern, and adopt AI-enabled solutions by creating the tools, content, playbooks, processes, and decision frameworks that shape how AI work gets done. The position influences productivity, decision quality, innovation velocity, stakeholder confidence, and the readiness of business and IT teams to scale AI responsibly. Work produced by this role may affect multiple functions, brands, business users, and future AI initiatives.
- Leadership: This role will lead one to two AI analysts; leadership is demonstrated through expertise, ownership, influence, coaching, and the ability to drive alignment across a matrixed organization. The Manager is expected to support the Director with strategic and operational priorities, represent AI Innovation in cross-functional working sessions, guide stakeholders through complex decisions, set standards for high-quality deliverables, and help raise the capability of the team and organization. The role requires comfort leading through ambiguity, building trust with senior stakeholders, and creating structure where processes do not yet exist.
Essential/Minimal Qualifications:
- Bachelor's Degree in Business, Data Science, Computer Science, Information Systems, Engineering, Analytics, or a related field; or equivalent combination of education and experience.
- Strong working knowledge of generative AI, analytics, automation, prompt engineering, workflow design, AI-enabled business tools, and emerging AI trends.
- Demonstrated ability to translate ambiguous business needs into structured use cases, requirements, solution concepts, prototypes, documentation, adoption content, and leadership recommendations.
- Proven ability to build or configure practical tools using platforms such as Microsoft Copilot, Power Platform, low-code/no-code tools, analytics platforms, AI assistants, workflow automation tools, or similar technologies.
- Ability to evaluate AI opportunities for business value, feasibility, risk, user readiness, scalability, and alignment to broader priorities.
- Strong written communication skills with the ability to create polished playbooks, training materials, prompt guides, executive materials, business cases, and stakeholder communications.
- Strong facilitation and stakeholder-management skills, with the ability to lead discovery sessions, demos, workshops, prioritization discussions, and cross-functional working sessions.
- Ability to partner effectively with IT, data, security, privacy, architecture, business operations, and functional teams to support responsible experimentation and scalable delivery.
- Ability to work independently, manage competing priorities, create structure in ambiguous environments, and drive work from idea through prototype, recommendation, adoption plan, or production handoff.
Essential Experience Required:
- 5+ years of progressive experience in analytics, data science, AI/ML, automation, product management, business transformation, process improvement, consulting, innovation, or a related field.
- Experience leading complex discovery efforts, defining use cases, gathering requirements, sizing business value, and developing practical solutions or prototypes for non-technical users.
- Experience supporting strategy, roadmap, portfolio management, executive reporting, or leadership decision-making in a technology, analytics, automation, transformation, or innovation environment.
- Experience creating scalable operating processes, playbooks, documentation standards, templates, or governance routines that improve consistency across a team or organization.
- Experience creating enablement content, training materials, communications, demos, or knowledge assets that help business users understand and adopt new tools or ways of working.
- Experience partnering with technology teams to clarify requirements, document assumptions, manage risks, align on production-readiness expectations, and support handoff from experimentation to implementation evaluation.
- Experience presenting recommendations, demos, trend insights, business cases, or strategy materials to leaders and cross-functional stakeholders.
- Experience in travel, hospitality, cruise, commercial operations, marketing, revenue, contact center, guest experience, onboard, or corporate operations preferred.
Travel: No or very little travel likely
Work Conditions: Work primarily in a climate-controlled environment with minimal safety/health hazard potential.
Physical Demands:. Must be able to remain in a stationary position at a desk and/or computer for extended periods of time.
** This position is classified as “in-office.” As an in-office role, it requires employees to work from a designated Holland America Line office in Miami/Doral, FL Monday through Thursday each week. Employees may work from their homes on Fridays.
Holland provides comprehensive and innovative benefits to meet your needs, including:
What You Can Expect
- Cruise and Travel Privileges for You and Your Family
- Health Benefits
- 401(k)
- Employee Stock Purchase Plan
- Training & Professional Development
- Tuition & Professional Certification Reimbursement
Our Culture…Stronger Together
Our highest responsibility and top priority is compliance, environmental protection and the health, safety and well-being of our guests, the people in the communities we touch and serve, and our shipboard and shoreside employees. Please visit our site to learn more about our Culture Essentials, Corporate Vision Statement and our Core Values at:https://www.hollandamerica.com/en_US/our-company/mission-values.html
Holland America is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances.
Americans with Disabilities Act (ADA)
Holland America will provide reasonable accommodations with the application process, upon your request, as required to comply with applicable laws. If you have a disability and require assistance in this application process, please contact [email protected]
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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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Holland America Line, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Holland America Line AI Hiring
Holland America Line has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Miami, FL, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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