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About This Role
The GCS – Security Architecture function secures Carnival Corp’s adoption of AI by ensuring GenAI, model, and agentic systems are deployed, validated, and operated against published security standards. This position is a hands\-on engineering role focused on AI security posture, model validation, and securing the inference layer. The AI Engineer builds and operates the controls, tooling, and automation that keep AI workloads secure across the global brands—validating models and pipelines, enforcing identity and data protection controls, and partnering with engineering and Cloud Security teams to reduce risk to Carnival Corp and customer information assets.
Essential Functions:
- Implement and operate AI security posture management (AISPM/DSPM) across model and inference infrastructure; identify misconfigurations, drift, and risk in AI workloads and drive remediation.
- Design, build, and run model validation and evaluation harnesses to test AI models and pipelines for robustness, data leakage, and unexpected behavior before and after deployment.
- Build and maintain inference\-layer guardrails and controls (input/output filtering, rate and access controls) for GenAI and agentic applications.
- Define and enforce PHI/PII and data\-protection controls, and identity/access controls (least\-privilege, agent\-to\-agent authentication) for AI systems.
- Partner with Cloud Security to establish and validate guardrails in AWS and Azure for AI workloads.
- Develop scripts, automation, and tooling (primarily Python) to integrate AI security controls and automated testing into CI/CD pipelines and engineering workflows.
- Document secure architecture patterns, anti\-patterns, and reference materials for model deployment and inference, and provide remediation guidance to engineering teams.
- Research, test, and pilot AI security tools and utilities aligned to the NIST AI RMF and related standards.
- Transition matured AI security monitoring to security operations teams.
Knowledge, Skills \& Abilities:
- Strong scripting and automation skills (especially Python) for security tooling, evaluation harnesses, and automated testing of AI systems. Hands\-on experience validating AI/ML models and securing the inference layer. Working knowledge of Identity and Access Management and data security/privacy controls (PHI/PII, encryption). Applied understanding of the NIST AI RMF and related AI security frameworks. Ability to communicate AI security concepts in non\-technical terms to business stakeholders.
Essential/Minimum qualifications:
- Bachelor's degree in Cyber Security, Computer Science, Artificial Intelligence, or a related field (Master's a plus).
- Cloud environment (Azure \& AWS) security fundamentals. Strong programming/scripting ability (Python). Knowledge of machine learning concepts, secure MCP configuration, Application/API security, and data protection.
Essential experience required
- 3\+ years (mid\-level) in Cyber Security, AI/ML, or security engineering in a large enterprise setting.
- Identity and Access Management fundamentals. Hands\-on experience with model validation, security tooling and automation, and applying the NIST AI RMF to AI workloads.
Travel: None
Work Conditions:Work primarily in a climate\-controlled environment with minimal safety/health hazard potential.
Physical Demands. Work primarily in a climate\-controlled environment with minimal safety/health hazard potential.
This position is classified as “in\-office.” As an in\-office role, it requires employees to work from a designated Carnival 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.
Offers to selected candidates will be made on a fair and equitable basis, taking into account specific job\-related skills and experience.
At Carnival, your total rewards package is much more than your base salary. All non\-sales roles participate in an annual cash bonus program, while sales roles have an incentive plan. Director and above roles may also be eligible to participate in Carnival’s discretionary equity incentive plan. Plus, Carnival provides comprehensive and innovative benefits to meet your needs, including:
- Health Benefits:
+ Cost\-effective medical, dental and vision plans
+ Employee Assistance Program and other mental health resources
+ Additional programs include company paid term life insurance and disability coverage
- Financial Benefits:
+ 401(k) plan that includes a company match
+ Employee Stock Purchase plan
- Paid Time Off
+ Holidays – All full\-time and part\-time with benefits employees receive days off for 8 company\-wide holidays, plus 2 additional floating holidays to be taken at the employee’s discretion.
+ Vacation Time – All full\-time employees at the manager and below level start with 14 days/year; director and above level start with 19 days/year. Part\-time with benefits employees receive time off based on the number of hours they work, with a minimum of 84 hours/year. All employees gain additional vacation time with further tenure.
+ Sick Time – All full\-time employees receive 80 hours of sick time each year. Part\-time with benefits employees receive time off based on the number of hours they work, with a minimum of 60 hours each year.
- Other Benefits
+ Complementary stand\-by cruises, employee discounts on confirmed cruises, plus special rates for family and friends
+ Personal and professional learning and development resources including tuition reimbursement
+ On\-site Fitness center at our Miami campus
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About Us
Carnival Corporation \& plc is the world’s largest leisure travel company, our mission to deliver unforgettable happiness to our guest through our diverse portfolio of leading cruise brands and island destinations, including Carnival Cruise Line, Holland America Line, Princess Cruises, and Seabourn in North America and Australia; P\&O Cruises and Cunard Line in the United Kingdom; AIDA in Germany; Costa Cruises in Southern Europe.
Join us and embark on a career that offers not only the chance to grow professionally but also the opportunity to be part of a global community that makes a difference.
In addition to other duties/functions, this position requires full commitment and support for promoting ethical and compliant culture. More specifically, this position requires integrity, honesty, and respectful treatment of others, as well as a willingness to speak up when they see misconduct or have concerns.
Carnival Corporation \& plc and Carnival Cruise Line is an equal employment opportunity/affirmative action employer. In this regard, it does not discriminate against any qualified individual on the basis of sex, race, color, national origin, religion, sexual orientation, age, marital status, mental, physical or sensory disability, or any other classification protected by applicable local, state, federal, and/or international law.
https://www.dol.gov/sites/dolgov/files/WHD/legacy/files/eppac.pdf
https://www.dol.gov/sites/dolgov/files/WHD/legacy/files/fmlaen.pdf
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Carnival Cruise 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Carnival Cruise Line AI Hiring
Carnival Cruise Line has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Miami, FL, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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