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About This Role
Cruise Planners is the largest franchisor of home-based travel agencies across the country that independently book cruises, tour, and land vacations. Cruise Planners has achieved Top Producer status with virtually every cruise line, tour, and resort company. Cruise Planners has been named the No.1 Cruise Tour / Travel Agency by Entrepreneur Magazine for the past 10 years and recently featured in Entrepreneur as one of the top 30 franchise innovators in technology. Cruise Planners positions franchise owners for success by providing them with innovative marketing, booking and lead-generating tools as well as professional development and hands-on training with the industry’s top executives. Cruise Planners Home Office has won awards such as Coolest Offices and Top Places to Work in South Florida by Sun Sentinel and South Florida Business Journal.
Candidates must be authorized to work for ANY employer in the United States. We are unable to sponsor or take over sponsorship of an employment Visa Job Summary The Data & AI Engineer designs, builds, and operates the data and AI systems behind Cruise Planners' analytics, advisor-facing products, and internal automation. The role combines production data engineering on AWS (Glue, Redshift, Step Functions, Lambda, S3) with applied generative-AI work on Amazon Bedrock (Data Automation, Knowledge Bases, AgentCore) and the Strands Agents SDK.
Day-to-day development is conducted through agentic coding tooling — Claude Code with MCP — used as a primary engineering surface, not as autocomplete, with the pace and scope of delivery expected to exceed what traditional tooling supports. The team is small: engineers scope, build, deploy, document, and operate their own work end to end.
The items listed below are intended to provide an overview of the essential functions of the job. This is not an exhaustive list of all functions and responsibilities that the position may be required to perform.
Responsibilities
- Design and operate data pipelines on AWS — Glue, Step Functions, Lambda, EventBridge, S3 — feeding the Redshift data warehouse.
- Stand up and operate containerized production pipelines on AWS Fargate / ECS (scheduled ingest or scrape, enrich, deliver) with CloudWatch alarms and health monitoring.
- Design dimensional data models — facts, conformed dimensions, and analytics marts — on Redshift over a zero-ETL replica of the operational source.
- Implement document-extraction pipelines using Amazon Bedrock Data Automation blueprints and deliver structured output to downstream consumers via webhook or queue.
- Build and operate Bedrock Knowledge Bases for retrieval-augmented generation: source preparation, indexing, retrieval evaluation, and cost / latency tuning.
- Build agents using the Strands Agents SDK on Bedrock AgentCore — supervisor / sub-agent topologies, tool definitions, and deployment hardening.
- Build applied-LLM data products for reporting, search, and agentic operations.
- Deploy and operate services on AWS end to end, including internal web apps and demos.
- Build internal tooling and integrations, including MCP servers.
- Prototype and ship new AI-powered applications for advisors and internal teams.
- Maintain operational quality — logs, alarms, runbooks, on-call response, post-incident notes — and continuously monitor quality and compliance with data-privacy regulations.
- Conduct day-to-day development through Claude Code with project-scoped MCP servers, document decisions and architectures in the team wiki.
Competencies
Technical (builds with)
- AWS data and serverless services.
- Amazon Bedrock and AgentCore Runtime.
- Python (primary); SQL (baseline).
- Data warehousing and dimensional modeling.
- Agent and LLM frameworks.
- Infrastructure-as-code.
Tooling (works with)
- Claude Code as the primary development surface, including parallel sessions.
- MCP servers (AWS, Excolo, Atlassian) as deterministic tool routing for work that previously spanned terminal, IDE, and browser.
- Comfort treating the model as a collaborator — generating alternatives, having design conversations, validating choices before committing — rather than as autocomplete.
General
- Writes documentation a teammate can pick up and act on without a meeting.
- Explains trade-offs in plain language to non-engineers (Marketing, Operations, Finance).
- Strong problem-solving, communication, and collaboration skills.
Requirements
- Bachelor's degree in Computer Science, Data Science, Statistics, Engineering, or a related field. Master's preferred at the Senior level.
- Relevant production experience scaled to level (Data & AI Engineer I / II / Senior).
- Authorization to work in the United States without sponsorship.
Levels
Placement is by experience, evidenced output, and judgment scope — not by years alone.
- Data & AI Engineer I — Executes scoped work end to end under guidance; ships well-defined pipelines, blueprints, or Knowledge Base load jobs.
- Data & AI Engineer II — Owns workstreams end to end (design, build, deploy, operate) and makes architecture choices within an established pattern; writes the playbook a peer can ship from.
- Senior Data & AI Engineer — Owns multi-system architecture; stands up a new pattern (e.g., AgentCore deployment, multi-agent topology) without precedent; coaches peers; represents the team in cross-functional design. Operates across the full stack — Fargate production pipelines, inbound AWS deployments, applied-LLM data products, and end-to-end agent architecture.
This is a hybrid role (Remote: Mondays and Fridays, Onsite: Tuesdays, Wednesdays and Thursdays) Cruise Planners provides equal employment opportunities (EEO) to all team members and applicants for employment without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, disability, genetic information, marital status, or status as a covered veteran in accordance with applicable federal, state and local laws. Cruise Planners complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities or team members. This policy applies to all terms and conditions of employment.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Cruise Planners, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Cruise Planners AI Hiring
Cruise Planners has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Coral Springs, FL, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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