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About This Role
One of the best\-known names in cruising, Princess is the world’s leading international premium cruise line and tour company, carrying millions of guests each year to hundreds of destinations around the globe. We give our guests the Medallion Class experience others simply can’t. The Love Boat promises something for everyone.
We are looking to hire a Principal AI Forward Deployed Engineer. The Principal AI Forward Deployed Engineer is embedded directly with business units across the organization to quickly prototype, deploy, customize, and operationalize AI solutions that solve critical business problems. This role bridges the gap between the AI/Data team and the business—translating ambiguous challenges into working AI applications that deliver measurable value.
This is a full\-stack engineering role with deep backend expertise. You will design and build production\-grade applications, APIs, and data pipelines that bring AI capabilities to life. Proficiency in modern application development, containerization, orchestration, and event\-driven architectures is essential.
Unlike traditional engineering roles, the Principal AI Forward Deployed Engineer operates at the intersection of technical execution and business problem\-solving. You will work side\-by\-side with stakeholders in Guest Services, Revenue Management, Operations, Food \& Beverage, and other functions to rapidly prototype, deploy, and iterate on AI solutions in real\-world environments—including shipboard systems.
This role requires a builder's mindset: someone who can scope a vague problem, architect a robust solution, write production\-grade code, and ship it fast. You will bring field insights back to the core AI team, identifying reusable patterns and influencing the product roadmap based on what you learn in the field.
Here’s a summary of what Princess is looking for in a Principal AI Forward Deployed Engineer. Is this you?
Responsibilities:
- AI Application Development \& Deployment: Architect, build, and deploy enterprise\-grade AI\-powered applications using modern backend technologies (Python, Node.js, FastAPI, Express). Design and implement robust APIs and microservices architectures that integrate AI/ML models—including LLMs and agentic systems—with business systems at scale. Lead containerization strategies using Docker and manage complex deployments via Kubernetes (EKS/ECS) with a focus on reliability, scalability, and performance. Design and implement event\-driven architectures using Kafka or similar streaming platforms for real\-time data processing and AI inference. Take full ownership of end\-to\-end delivery from technical scoping and architecture design through production deployment, monitoring, optimization, and ongoing operational excellence.
- Rapid Prototyping \& Problem Discovery: Deconstruct ambiguous, complex business problems into actionable AI solutions by deeply understanding operational context, system constraints, and stakeholder priorities. Rapidly build proof\-of\-concept applications using appropriate technology stacks to validate approaches and demonstrate business value. Architect scalable, production\-ready solutions that account for performance, reliability, security, and maintainability from inception. Lead iterative development cycles based on user feedback, refining solutions until they deliver measurable, quantifiable business impact. Serve as a trusted advisor to business units on what is technically feasible and strategically valuable.
- Stakeholder Engagement \& Technical Translation: Serve as the senior technical point of contact and trusted advisor for business stakeholders during AI deployments. Communicate complex technical concepts—including architecture decisions, trade\-offs, risks, and recommendations—to executive leadership and non\-technical audiences with clarity and confidence. Lead cross\-functional collaboration with data scientists, data engineers, platform teams, infrastructure teams, microservice teams, security, privacy, and product managers to ensure solutions meet rigorous technical standards and business objectives. Build and maintain strong relationships with business partners through consistent delivery, transparent communication, and a demonstrated commitment to their success.
- Field Insights \& Platform Feedback: Champion continuous improvement by bringing strategic learnings from field deployments back to the core AI/Data and Platform teams. Identify opportunities to improve tools, infrastructure, and reusable components that benefit the broader organization. Author and maintain comprehensive documentation including solution architectures, design patterns, and operational runbooks that enable knowledge transfer and accelerate future deployments. Proactively identify gaps in platform capabilities (CI/CD, observability, infrastructure, developer experience) and advocate for improvements with supporting business justification. Define and elevate engineering standards and best practices across the AI organization, mentor junior and mid\-level engineers on these standards.
Knowledge \& Skills:
- Scope: The Senior Principal AI Forward Deployed Engineer operates as a technical leader across multiple business units—including Strategy \& Analytics, Finance, Guest Services, Revenue Management, Operations, Marketing, and beyond—embedding directly with teams to architect and deploy enterprise\-grade AI solutions in both shoreside and shipboard environments. This role commands the full technology stack, from backend application development (Python, Node.js, APIs, microservices) to infrastructure (Docker, Kubernetes, Kafka) and advanced AI/ML integration including LLMs and agentic systems. The incumbent serves as the primary technical authority during AI deployments, working cross\-functionally with data scientists, data engineers, platform teams, infrastructure teams, security, privacy, and product managers. Engagements typically involve weeks to months of focused, high\-stakes collaboration with business units to deliver production\-ready AI solutions that drive measurable outcomes.
- Impact: The Senior Principal AI Forward Deployed Engineer plays a pivotal role in transforming AI prototypes into scalable, production\-grade solutions that deliver quantifiable business value. By embedding directly with business units and taking full ownership of end\-to\-end delivery, this position significantly accelerates the organization's ability to realize returns on AI investments. The impact extends to driving operational efficiency gains, elevating guest experiences, and enabling data\-driven decision\-making through robust, deployed AI applications. This role serves as the critical bridge between technical capabilities and real\-world business needs, ensuring AI initiatives advance beyond proof\-of\-concept to deliver innovation, competitive advantage, and tangible outcomes—including revenue growth, cost reduction, improved satisfaction scores, and new capabilities that were previously unattainable. The incumbent's contributions multiply across the organization as reusable patterns, best practices, and platform improvements benefit future deployments.
- Problem Solving: This role demands expert\-level analytical and technical skills to address the most complex, ambiguous challenges in AI deployment and application development. The Senior Principal AI Forward Deployed Engineer must deconstruct vague business problems into actionable technical solutions by deeply understanding operational context, system constraints, data availability, and stakeholder priorities. Problem\-solving involves conducting root cause analysis, architecting scalable and maintainable solutions, resolving critical production issues under pressure, and iterating approaches based on real\-world feedback. The ability to navigate high\-stakes trade\-offs—speed vs. scalability, custom vs. reusable, perfect vs. good enough—is essential. The incumbent must also solve for complex real\-world constraints including legacy system integration, shipboard connectivity limitations, distributed/offline environments, security requirements, and aggressive timelines while delivering solutions that meet rigorous technical standards and business objectives. This role serves as the escalation point for the most challenging technical blockers across AI deployments.
- Leadership: While this position does not have direct reports, it demonstrates senior\-level leadership through technical ownership, strategic influence, and cross\-functional impact. The Senior Principal AI Forward Deployed Engineer leads by owning end\-to\-end delivery of AI solutions, making critical architecture decisions, setting quality standards, and serving as a technical role model during engagements. By partnering with data scientists, engineers, and business stakeholders, the role builds alignment, removes blockers, and drives projects forward without formal authority. Leadership is further demonstrated through mentoring junior and mid\-level engineers, establishing best practices and reusable patterns that scale across the organization, advocating for platform and tooling improvements based on field insights, and communicating effectively with executives on solution progress, technical trade\-offs, and strategic recommendations. The incumbent champions responsible AI deployment influences the organization's AI roadmap through field\-driven insights, and guides business units toward leveraging AI technologies effectively, ethically, and strategically.
Requirements:
- Bachelor's degree in Computer Science, Software Engineering, Data Science or related field
- Master's degree preferred but not required
Certifications (required at least 1\):
- AWS certifications (Solutions Architect, Developer Associate, Machine Learning Specialty)
- Kubernetes certifications (CKA, CKAD)
- Relevant AI/ML or cloud certifications
Minimum Experience:
- 6\-8\+ years of software engineering experience with a focus on backend/full\-stack development
- 4\+ years deploying AI/ML solutions in production environments
- Led/architected 3\+ production AI systems
- Strong hands\-on experience with Docker, Kubernetes, and cloud\-native architectures
- Experience building and operating event\-driven or streaming data systems (Kafka, Kinesis)
- Track record of delivering technical solutions in ambiguous, fast\-moving environments
- Track record leading technical engagements with senior/executive stakeholders.
- Mentored junior engineers; led cross\-functional delivery teams.
Preferred Experience:
- Experience in a Forward Deployed Engineer, Solutions Engineer, or Technical Consultant role
- Experience building generative AI applications, LLM integrations, or agentic AI solutions
- Background in travel, hospitality, or cruise industry
- Experience with shipboard or distributed/disconnected computing environments
- Contributions to platform engineering, developer tooling, or infrastructure automation
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 Princess office Monday through Thursday each week. Employees may work from their homes on Fridays. Candidates must be located in (or willing to relocate to) the area.
Princess 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
- Rewards \& Incentives
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: princess.com/en\-us/company\-information
Princess 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)
Princess 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].
\#PCL
\#LI\-Hybrid
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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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At HA 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. Senior-level AI roles across all categories have a median of $227,400.
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.
HA Group AI Hiring
HA Group 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, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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