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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.
The Senior Engineer, ITSM Automation \& AI Platform is the operational and engineering engine behind the company's ITSM transformation agenda — responsible for designing, building, governing, and continuously evolving the automation architecture that underpins how technology incidents, changes, problems, and service requests are managed across the enterprise fleet and shoreside organization.
This is not a configuration\-only role. The Senior Engineer owns the full technical lifecycle of the ServiceNow platform — from scripted automation and AI agent deployment to deep Dynatrace integration and real\-time observability — and translates operational data into actionable intelligence that reaches leadership. In an environment where ships operate continuously across international routes with constrained connectivity windows, the reliability, intelligence, and automation maturity of the ITSM platform directly impacts guest experience, crew productivity, and the company's ability to sustain maritime operations at scale.
This hire owns the SNOW/Dynatrace automation function exclusively — this function does not sit within Platform Engineering. The Senior Engineer serves as the subject matter expert, the builder, and the custodian of a platform undergoing one of the most significant capability shifts in its history: the emergence of agentic AI and autonomous workflows that replace manual L1 intervention, enrich incident data from observability signals, and enable zero\-outage operational outcomes. The individual who succeeds in this role will be technically exceptional, strategically aware, and deeply committed to turning ITSM from a reactive discipline into a proactive, self\-healing function.
Here’s a summary of what Princess is looking for in a Senior Engineer, ITSM Automation \& AI Platform. Is this you?
Responsibilities:
ServiceNow Platform Engineering \& AI Automation (30%)
- The Senior Engineer owns the full technical development and configuration lifecycle of the ServiceNow platform at Princess Cruises. This encompasses the design and implementation of Business Rules, Script Includes, Client Scripts, UI Policies, Flow Designer workflows, and custom application logic that automates ITSM processes end\-to\-end. The incumbent is accountable for the reliability, performance, and upgrade readiness of the platform at all times.
- Architect, build, and maintain automation workflows across Incident, Change, Problem, and Service Catalog modules using Flow Designer, Business Rules, and scripted automation.
- Design, configure, and govern ServiceNow Now Assist AI capabilities — including conversational AI, generative summarization, and intelligent routing — within production ITSM workflows.
- Deploy, configure, and operationalize ServiceNow Autonomous Workforce AI Specialists, beginning with the Level 1 Service Desk AI Specialist, and extending to IT Operations and additional specialist types as the platform evolves.
- Maintain and optimize CMDB integrity, ensuring configuration item relationships accurately reflect the fleet and shoreside infrastructure landscape.
- Own platform upgrade planning, regression testing (ATF), and remediation cycles to ensure continuity across ServiceNow release cadences.
- Build and maintain Service Portal components, Record Producers, and self\-service catalog items that reduce friction for end users across fleet and shoreside populations.
Dynatrace – ServiceNow Integration \& AIOps (25%)
- A defining capability of this role is the ownership of the Dynatrace–ServiceNow integration stack. As the two platforms deepen their strategic partnership toward autonomous IT operations, this engineer is responsible for architecting and maintaining the connective tissue between real\-time observability signals and actionable ITSM workflows. The goal is to move Princess Cruises from reactive incident management to proactive, AI\-driven operational intelligence.
- Design and maintain the bidirectional integration between Dynatrace and ServiceNow ITOM, ensuring observability events are automatically ingested, enriched, deduplicated, and routed to the correct ITSM workflow without human triage.
- Configure and optimize Dynatrace–ServiceNow incident creation pipelines with context enrichment — embedding root\-cause descriptions, affected service topology, and remediation recommendations directly into SNOW incident records.
- Leverage Now Assist and Dynatrace AI integration to enable administrators to query Dynatrace insights from within the ServiceNow interface, reducing context switching and accelerating MTTR.
- Maintain and evolve the unified dashboard layer that surfaces Dynatrace metrics and charts directly within ServiceNow, eliminating administrative silos between observability and operations tooling.
- Partner with Platform Engineering and network operations to ensure MID Server health, event routing rules, and alert suppression policies are correctly configured and maintained.
- Stay current on the Dynatrace–ServiceNow joint roadmap and proactively identify new integration capabilities that can be adopted to advance the autonomous operations agenda.
ITSM Process Automation \& Knowledge Management (20%)
- This engineer is accountable for systematically reducing the volume of manual work performed across the ITSM function by identifying, designing, and deploying automations that handle repetitive, rules\-based work. The engineer partners closely with the ITSM TIM team, Change Management coordinators, and Problem Management leads to translate operational patterns into durable automation logic and institutional knowledge.
- Identify and implement end\-to\-end automation for high\-frequency, low\-complexity incident types — replacing manual L1 triage with AI\-driven diagnosis, action, documentation, and knowledge base update.
- Build and maintain automated SLA monitoring and breach notification workflows that proactively surface at\-risk tickets to the right stakeholders before SLAs are violated.
- Own the Knowledge Management automation strategy — including AI\-assisted article creation, resolution pattern capture from closed incidents, and knowledge base quality governance.
- Develop and maintain Change Management automation: pre\-approval checks, conflict detection, risk scoring integration, and post\-implementation review automation within the CAB workflow.
- Maintain a master version registry across the fleet and ecosystem — tracking software versions across all vessel and shoreside environments, surfacing discrepancies, and feeding this data into change risk assessments.
- Collaborate with the Sr. Manager to define and refine automation KPIs, publishing regular metrics on automation deflection rates, MTTR improvement, and L1 self\-resolution rates.
Data, Reporting \& Executive Intelligence (10%)
- The Senior Engineer plays a central role in transforming raw ITSM and observability data into structured, executive\-ready intelligence. This individual builds and maintains the reporting layer that feeds the Sr. Manager’s dashboards, the CIO briefings, and the operational visibility tools used by the ITSM support team and fleet leadership.
- Build, maintain, and evolve ServiceNow Performance Analytics dashboards, scheduled reports, and real\-time views for incident volume, SLA performance, MTTR trends, automation deflection, and change risk.
- Design and deliver the upstream reporting pipeline that surfaces ITSM operational data to technology leadership — structured for the CIO, VP Technology, and C\-suite in a format that connects technology outcomes to business impact.
- Maintain audit trails and governance documentation for all AI\-driven automation decisions — ensuring accountability, traceability, and compliance with enterprise data governance requirements.
- Support the Sr. Manager in producing financial and ROI analyses that quantify the impact of automation initiatives — including cost\-per\-incident reduction, staffing efficiency gains, and automation coverage percentage.
Integration Architecture \& REST API Development (10%)
- This role requires fluency in enterprise integration patterns. The Senior Engineer designs and owns REST API integrations between ServiceNow and third\-party platforms, builds data transformation logic, and ensures that all integration surfaces are documented, monitored, and resilient.
- Design, build, and maintain REST API integrations between ServiceNow and external platforms including Dynatrace, HR systems, guest experience platforms, maritime operations tools, and enterprise communication channels.
- Develop and maintain Transform Maps, Import Sets, and scripted data migration pipelines to ensure CMDB and asset data remains accurate as the technology environment evolves.
- Maintain integration documentation standards — ensuring all APIs, event schemas, authentication methods, and data flows are current, auditable, and accessible to the broader ITSM team.
- Evaluate and adopt integration patterns from the ServiceNow Autonomous Workforce framework, including agentic AI handoff protocols and governed execution trails.
Knowledge \& Skills:
- SCOPE: This role operates across the full breadth of the Princess Cruises technology environment — spanning 15\+ active vessels, multiple shoreside offices, and a fleet IT complement of 51 technology professionals at sea. The Senior Engineer's work directly influences how technology incidents, changes, problems, and service requests are managed across maritime, hotel, technical, HR, entertainment, and communications domains. The automation architecture owned by this role has global operational reach and a direct impact on guest experience and crew productivity.
- PROBLEM SOLVING: This role requires sophisticated, autonomous problem solving in a complex, always\-on environment. The engineer must diagnose platform deficiencies, anticipate integration failure points before they become operational incidents, and design automation logic that accounts for edge cases in a distributed maritime environment with constrained connectivity. Problems routinely require original analysis — there is no off\-the\-shelf playbook for autonomous ITSM in a maritime context.
- IMPACT: The Senior Engineer's outputs directly determine the maturity, reliability, and intelligence of the ITSM function at Princess Cruises. Poor automation design ripples into degraded incident response times, increased L1 labor cost, and executive visibility gaps. Excellent automation design measurably reduces MTTR, deflects volume from the service desk, and produces the data that enables strategic technology decisions at the C\-suite level.
- LEADERSHIP: This is a senior individual contributor role with no direct reports. The incumbent is expected to self\-direct entirely, manage their own backlog against strategic priorities set by the Sr. Manager, and function as the technical authority for all ServiceNow and Dynatrace automation questions across the ITSM practice.
- KNOWLEDGE: Deep expertise in ServiceNow platform architecture, GlideScript, Flow Designer, and ITSM module configuration. Strong working knowledge of Dynatrace AIOps capabilities and the Dynatrace–ServiceNow integration suite. Solid understanding of REST API design principles, JSON/XML data transformation, and enterprise integration patterns. Familiarity with ITIL v4 practices — particularly Incident, Change, Problem, and Continual Improvement. Knowledge of AI governance principles as applied to enterprise agentic workflows.
- SKILLS: ServiceNow platform engineering at production grade. JavaScript/GlideScript fluency (server\-side and client\-side). REST API design and integration development. Executive\-quality reporting and dashboard design in ServiceNow Performance Analytics. Strong written and verbal communication — able to translate technical architecture into business language for leadership audiences. Documentation discipline: all automation logic, integrations, and AI agent configurations must be versioned and auditable.
- ABILITIES: Ability to prioritize and self\-manage a complex technical backlog in a fast\-moving environment. Capacity to operate as the sole owner of a mission\-critical platform while maintaining reliability standards. Ability to collaborate with xOps TIM, Change Management, Platform Engineering, and fleet IT stakeholders across time zones. Demonstrated professionalism and composure in high\-visibility, high\-stakes operational situations. Ability to maintain reliable, consistent availability during scheduled maintenance windows and critical incident situations.
For all roles:
Knowledge: Understanding of workplace policies and procedures / Familiarity with team collaboration tools and techniques.
Skills: Strong time management and organizational skills
Abilities: Ability to maintain reliable and consistent attendance / Capacity to be punctual and meet deadlines / Ability to collaborate effectively with colleagues and work as part of a team / Demonstrated professionalism in all interactions and tasks.
Essential/Minimum Qualifications:
- Bachelor's degree in Information Systems, Computer Science, Information Technology, or equivalent field of study. Equivalent professional experience with demonstrated certifications will be considered in lieu of formal degree.
- Active ServiceNow Certified System Administrator (CSA) — required or must be obtained within 6 months of hire.
- Minimum 5 years of hands\-on ServiceNow development experience with production responsibility across ITSM modules (Incident, Change, Problem, Service Catalog, Knowledge Management).
- Demonstrated experience with ServiceNow automation technologies: Flow Designer, Business Rules, Script Includes, Client Scripts, UI Policies, UI Actions, Scheduled Jobs.
- Strong JavaScript proficiency — both server\-side (GlideScript) and client\-side scripting within the ServiceNow platform.
- Understanding of ITIL v4 frameworks — particularly Incident, Change, Problem, and Continual Improvement practices.
Minimum Experience (if preferred but not required, list as such):
- 5\+ years of hands\-on ServiceNow development with production responsibility across core ITSM modules. Experience must demonstrate depth — not just configuration but scripted automation, integration development, and platform governance.
- Proven track record designing and implementing REST API integrations between ServiceNow and enterprise third\-party platforms at scale.
- Hands\-on experience with ServiceNow CMDB management, import sets, transform maps, and data quality governance.
- Experience with ServiceNow Now Assist, AI\-assisted ITSM capabilities, or equivalent enterprise AI workflow tooling.
- Hands\-on experience with Dynatrace or equivalent AIOps/observability platform, including ITSM event integration.
- Experience working in complex, multi\-environment enterprise landscapes with distributed stakeholder groups.
STRONGLY PREFERRED:
- Active ServiceNow certifications: CIS\-ITSM and/or CIS\-ITOM, Certified Application Developer (CAD).
- Hands\-on experience with ServiceNow Autonomous Workforce AI Specialists or agentic AI agent configuration within enterprise workflows.
- Experience with the Dynatrace–ServiceNow integration suite (released November 2025\), including ITOM event ingestion, CMDB enrichment, and Now Assist AI connector.
- Secondary programming language proficiency: Python, JavaScript (Node.js), or TypeScript.
- Familiarity with DevOps tooling: Git, CI/CD pipelines, Docker, and automated testing frameworks in a ServiceNow development context.
- Experience in hospitality, maritime, travel, or other 24/7 operational industries where ITSM directly impacts guest or customer\-facing systems.
Minimum Qualifications (if preferred but not required, list as such):
Leadership \& Management:
- Proven ability to build, lead, and develop technical teams—hiring, coaching, performance management, and succession planning
- Experience setting strategy and a multi\-year roadmap and translating it into execution across multiple teams
- Experience managing budgets, resources, and vendor/contractor relationships
- Experience leading organizational change and driving enterprise adoption
- Business \& Strategic:
- Ability to translate business needs into actionable engineering plans
- Data Engineering:
- Strong background in ETL/ELT pipeline design, data modeling, and data architecture
- Proficiency in SQL and Python; experience with orchestration tools (e.g., Airflow, dbt)
- Experience with cloud data platforms/warehouses (e.g., Snowflake, BigQuery, Redshift, Databricks)
- Experience delivering dashboards/BI (e.g., Power BI, Tableau, Looker)
- Working knowledge of data governance, data quality, lineage, and security
AI / Machine Learning:
- Experience taking AI/ML use cases from concept through prototype to enterprise production
- Understanding of the ML lifecycle and MLOps (packaging, serving, monitoring, iteration)
- Familiarity with generative AI, LLMs, and/or agentic AI; awareness of AI governance and responsible\-AI practices
Education:
- Bachelor's degree in Computer Science, Data Engineering, Data Science, Engineering, or a related field; Master's (technical, analytics, or MBA) preferred but not required
- Cloud (AWS, Azure, or GCP), data platform (e.g., Snowflake, Databricks), or AI/ML certifications preferred
Essential experience required:
- 10\+ years in data engineering, analytics, or AI/ML, including hands\-on pipeline, data platform, and solution delivery
- 5\+ years leading and developing technical teams, including direct people\-management and, ideally, leading through team leads or other managers
- Proven track record setting strategy and a roadmap and translating business needs into delivered data products and AI solutions in production
- Experience taking solutions from prototype to enterprise\-scale production in partnership with IT, including driving adoption and change
- Experience managing a function's budget, resources, vendors, and operating model
- Experience establishing or operating data and/or AI governance and enterprise infrastructure
- Preferred: dual exposure to both data engineering and AI/ML deployment; experience standing up enterprise AI infrastructure or platforms; prior Director, Senior Manager, or equivalent leadership role within a Strategy/Analytics organization; background in travel, hospitality, or cruise industry
Travel: Less than 25% with shipboard 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 in South Florida Monday through Thursday each week. Employees may work from their homes on Fridays. Candidates must be located in (or willing to relocate to) the Miami/Ft. Lauderdale 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
\#LI\-SH1
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 Princess, 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.
Princess AI Hiring
Princess has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fort Lauderdale, 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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