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About This Role
AI Engineer
Remote \| Full\-Time
Kitson Yachts, a leading luxury yacht brokerage and advisory firm specializing in yacht sales, new construction, and charter, is seeking an AI Engineer to lead the development and implementation of artificial intelligence solutions across our business.
This role will focus on identifying opportunities to automate workflows, improve operational efficiency, enhance client engagement, and leverage AI to support sales, marketing, and business development initiatives. Working directly with company leadership, the successful candidate will play a key role in shaping how AI is integrated into a rapidly growing luxury business.
This is an ideal opportunity for a highly motivated engineer who enjoys building practical, business\-focused solutions and wants to make a measurable impact within an entrepreneurial environment.
Key Responsibilities
- Design, develop, and implement AI\-powered tools and automation systems across the organization
- Identify opportunities to streamline internal workflows through AI and process automation
- Build and maintain integrations between CRM platforms, marketing systems, communication tools, and AI applications
- Develop AI\-powered lead generation, prospecting, and client engagement workflows
- Create internal knowledge management systems utilizing Large Language Models (LLMs)
- Design custom GPTs, AI agents, and retrieval\-augmented generation (RAG) systems
- Develop dashboards, reporting tools, and business intelligence solutions
- Collaborate with sales, charter, marketing, and operations teams to identify automation opportunities
- Evaluate emerging AI technologies and recommend implementation strategies
- Improve data collection, organization, and utilization across company platforms
- Develop predictive analytics tools for sales performance, charter activity, and market intelligence
- Ensure all AI implementations align with data privacy, security, and compliance standards
- Train and support team members on AI\-powered systems and best practices
Experience \& Skills
- Bachelor's degree in Computer Science, Artificial Intelligence, Software Engineering, Data Science, or a related field
- 3\+ years of experience in AI engineering, machine learning, software development, or automation\-focused roles
- Strong understanding of Large Language Models (OpenAI, Anthropic, Gemini, etc.)
- Experience building AI agents, custom GPTs, workflow automations, and RAG systems
- Proficiency in Python and API development
- Experience with automation platforms such as Zapier, Make, n8n, or similar tools
- Familiarity with CRM platforms such as HubSpot
- Experience working with databases, embeddings, vector search, and AI infrastructure
- Knowledge of cloud platforms such as AWS, Azure, or Google Cloud preferred
- Strong analytical, problem\-solving, and project management skills
- Excellent written and verbal communication abilities
Preferred Qualifications
- Experience implementing AI solutions within sales, marketing, luxury, hospitality, real estate, or yachting businesses
- Experience with business intelligence and data visualization tools
- Knowledge of prompt engineering and LLM optimization techniques
- Experience building customer\-facing AI products and applications
- Understanding of data engineering and ETL workflows
Personal Attributes
- Entrepreneurial mindset with a passion for innovation
- Self\-starter capable of working independently in a remote environment
- Highly organized with strong attention to detail
- Curious, adaptable, and eager to stay ahead of emerging technologies
- Solutions\-oriented with strong business acumen
- Collaborative team player who can effectively communicate with both technical and non\-technical stakeholders
Why Join Kitson Yachts?
- Work directly with company leadership on high\-impact strategic initiatives
- Lead AI innovation within a fast\-growing luxury yacht brokerage
- Build systems that transform sales, marketing, charter, and operational workflows
- Enjoy the flexibility of a fully remote work environment
- Gain exposure to UHNW clients, global markets, and world\-class brands
- Opportunity to own and scale transformative technology projects from concept to deployment
- Competitive compensation with significant long\-term growth potential
Work Location: Remote
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Kitson Yachts, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Kitson Yachts AI Hiring
Kitson Yachts has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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