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About This Role
Lead, AI Search Optimization (AEO/GEO)
Job Summary
Dynamic and results\-driven AI Search \& SEO Strategist with a passion for maximizing visibility across emerging AI\-driven search ecosystems. This individual will serve as Princess Cruises' subject matter expert in Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO), ensuring brand visibility across large language models (LLMs), AI search platforms, and evolving discovery experiences.
As a key member of the eCommerce team, this role will own and drive the technical execution required to maximize visibility across AI\-powered search environments including Google AI Overviews, ChatGPT, Perplexity, and other AI answer engines. The position acts as the critical link between Engineering, Content, SEO, Analytics, and agency partners to ensure Princess Cruises remains discoverable as search behavior continues to evolve.
Key Responsibilities
AI Search \& AEO/GEO Strategy Execution (25%)
- Translate AI Search (AEO/GEO) strategies into actionable technical initiatives.
- Partner with SEO agencies and content teams to align traditional SEO and AI\-native optimization efforts.
- Identify opportunities to improve AI answer inclusion, citations, and brand visibility across AI search ecosystems.
AI Search / LLM Optimization (25%)
- Optimize content and site structure for LLM discoverability and citation.
- Improve:
+ Entity authority and topical relevance
+ Structured answer formats (FAQs, summaries, authoritative content blocks)
+ E\-E\-A\-T signals (Experience, Expertise, Authoritativeness, Trustworthiness)
- Identify gaps where Princess content is underperforming in AI\-generated responses.
Technical SEO \& Structured Data Ownership (20%)
- Own and enhance:
+ Schema markup and structured data (JSON\-LD, entity markup)
+ Crawlability, indexation, and internal linking improvements
- Ensure content is modular, machine\-readable, and optimized for AI consumption.
- Partner with engineering teams to improve:
+ Semantic HTML
+ Content hierarchy and formatting
+ Performance and accessibility signals
Performance Measurement \& Optimization (15%)
- Define and track KPIs related to:
+ Visibility within AI search experiences
+ Citation frequency and share of voice
+ Traffic and conversion impact
- Partner with Analytics teams to build AI\-search\-specific measurement frameworks.
- Lead testing, experimentation, and optimization initiatives.
Engineering Partnership \& Delivery (10%)
- Serve as the primary contact for AEO/GEO technical requirements.
- Translate business objectives into clear technical requirements and user stories.
- Influence engineering prioritization and backlog planning.
- Validate implementation quality and ensure successful delivery.
- Bridge agency recommendations with internal technical feasibility.
Additional Responsibilities (5%)
- Perform other duties as assigned.
- Adhere to all company policies, Code of Conduct requirements, audit procedures, and controls related to business systems and financial data.
Scope
The Lead, AI Search Optimization (AEO/GEO) is responsible for driving Princess Cruises' visibility across AI\-powered search and answer platforms, including Google AI Overviews, ChatGPT, Perplexity, and other LLM ecosystems.
This role operates at the intersection of eCommerce, Content, Technology, Analytics, and agency partnerships. The individual will lead initiatives focused on AI discoverability, content optimization, technical SEO enhancements, structured data strategy, and AI search performance.
As a highly collaborative individual contributor, this role will influence enterprise\-wide digital performance and support customer acquisition, engagement, conversion, and brand visibility objectives across Princess Cruises' global portfolio.
Problem Solving
This role addresses complex and evolving challenges within the AI search landscape where best practices, ranking signals, and success metrics continue to emerge.
The individual will analyze data from AI visibility platforms, SEO tools, analytics solutions, and search console reporting to:
- Diagnose visibility and citation challenges.
- Evaluate technical, content, and authority\-related factors impacting performance.
- Develop scalable improvement plans.
- Influence cross\-functional stakeholders to execute recommendations.
The role regularly exercises independent judgment in identifying opportunities that impact discoverability, traffic, and conversion performance.
Impact
This position serves as Princess Cruises' internal authority on AI Search Optimization and helps shape how the organization approaches discoverability across AI\-powered search experiences.
Success in the role directly contributes to:
- Increased AI search visibility
- Brand awareness
- Customer acquisition
- Traffic growth
- Engagement and conversion performance
The role helps ensure Princess Cruises remains competitive as consumers increasingly rely on AI\-powered discovery tools throughout their purchasing journey.
Leadership
Although this role does not have direct people management responsibilities, it requires significant cross\-functional leadership and influence.
The Lead, AI Search Optimization (AEO/GEO) will:
- Provide thought leadership on AI search best practices.
- Drive alignment across Content, Technology, Analytics, eCommerce, and agency teams.
- Communicate recommendations to technical and non\-technical stakeholders.
- Lead initiatives from concept through implementation.
- Influence priorities and decision\-making without direct authority.
Strong communication, stakeholder management, and project leadership skills are essential.
Required Qualifications
Education
- Bachelor's Degree in Marketing, Business, or a related field.
Experience
- 3\+ years of digital marketing experience with a focus on SEO.
- 1\+ year of hands\-on AEO/GEO optimization experience.
- Experience working with AI\-driven search ecosystems and emerging discovery platforms.
- Strong understanding of how SEO, AEO, and GEO contribute to broader digital marketing strategies.
Technical Skills
- Adobe Analytics
- Google Analytics 4 (GA4\)
- Google Search Console (GSC)
- HTML/CSS
- Basic JavaScript
- WordPress
- Adobe Experience Manager (AEM) or comparable CMS
- SEO, AEO, and GEO tools and best practices
Core Competencies
- Exceptional written and verbal communication skills.
- Strong analytical and problem\-solving abilities.
- Ability to translate business objectives into technical execution plans.
- Strong collaboration and stakeholder management capabilities.
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 Delan Associates, Inc, 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.
Delan Associates, Inc AI Hiring
Delan Associates, Inc has 9 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Software Engineer. Positions span Remote, US, Charlotte, NC, US, 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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