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About This Role
### POSITION OBJECTIVE
Offers exceptional customer service while selling merchandise, making court reservations and assisting management in merchandising, inventory control, and special events preparation. Candidate will also perform general court maintenance and repair of the tennis courts.
### ESSENTIAL JOB FUNCTIONS
- Responsible for daily maintenance of the outside operation which includes \- Cleanliness of the courts, facility, and equipment daily preventive maintenance, refurbishing clay and repair of irrigation (sprinklers).
- To perform daily operating procedures for the clay courts, which include sweeping, lining, watering, filling water, emptying the trashcans.
- Responsible for working closely with the pro shop staff on all court maintenance activities
- Responsible for cleanliness of indoor racquetball courts, mini golf, outdoor volleyball, shuffleboard, and basketball.
- Assists guests/members by booking court times for tennis, racquetball and instruction.
- Promotes tennis activities to guests/members when in shop and thru telephone and email communication.
- Assists with tournaments and socials.
- Performs opening and closing procedures for the shop or store and general cleaning of shop.
- Works closely with court maintenance staff regarding playability of courts and ensuring guest/member requests are exceeded.
- Assists tennis professionals with scheduling instruction.
- Responsible for accountability of all sales and money transactions made. Collects applicable fees from patrons as they arrive.
- Takes reservations for Tennis Camp and Camp Nessie and advises Recreation staff.
- Assists with fishing rentals and mini golf/activity area equipment.
- Assist with receiving merchandise and creating appealing displays
- Assists with monthly physical inventories.
In addition to performance of the essential functions, this position may be required to perform a combination of the following supportive functions, with the percentage of time performing each function to be solely determined by the manager based upon the particular requirements of the hotel:
- Maintains flexibility to take on new and different tasks as directed by the Department Manager.
- Incorporates safe work practices in job performance.
- Perform other duties and handle projects as assigned by Manager
### EDUCATION/EXPERIENCE
High school diploma or general education degree (GED); or one to three months related experience and/or training; or equivalent combination of education and experience.
### REQUIREMENTS
- Must be able to speak, read, write and understand English.
- Must be able to read and write to facilitate the communication process.
- Requires good communication skills, both verbal and written.
- Must possess basic computational ability.
- Must possess computer skills, including, but not limited to, accounting programs, Microsoft Office.
- Excellent interpersonal and sales\-related skills.
- May be required to apply common sense understanding to carry out instructions furnished in written, oral, or diagram form; to deal with problems involving several concrete variables in standardized situations.
### PHYSICAL DEMANDS
- May be required to apply common sense understanding to carry out instructions furnished in written, oral, or diagram form to deal with problems involving several concrete variables in standardized situations.
- Some work tasks are performed indoors. Most maintenance tasks are performed outdoors.
- Must be able to sit at a desk for up to 8 hours per day if handling pro shop duties..
- Must be able to stand and/or walk for up to 8 hours per day.
- Requires grasping, writing, typing, standing, walking, repetitive motions, listening and hearing ability and visual acuity.
- Talking and hearing occur in the process of communicating with others.
- Requires manual dexterity to use and operate all necessary equipment.
- Must have finger dexterity to be able to operate office equipment such as computers, printers, multi\-line phones, filing cabinets, photocopiers and other office equipment as needed.
- Responsible for daily maintenance of the outside operation which includes \- Cleanliness of the courts, facility, and equipment daily preventive maintenance, refurbishing clay and repair of irrigation (sprinklers).
- To perform daily operating procedures for the clay courts, which include sweeping, lining, watering, filling water, emptying the trashcans.
- Responsible for working closely with the pro shop staff on all court maintenance activities
- Responsible for cleanliness of indoor racquetball courts, mini golf, outdoor volleyball, shuffleboard, and Pickleball.
- Court maintenance tasks are performed mainly outdoors.
- Must be able to exert well\-paced ability to reach other departments of the hotel on a timely basis.
- Must be able to lift up to 50 lbs. on a regular basis.
- Requires grasping, writing, standing, sitting, walking, repetitive motions, listening and hearing ability and visual acuity.
- Talking and hearing occur continuously in the process of communicating with guests, supervisors and other employees.
- Vision occurs continuously with the most common visual functions being those of near vision and depth perception.
- Must be able to operate golf cart and court equipment.
- In addition to performance of the essential functions, this position may be required to perform a combination of the following supportive functions, with the percentage of time performing each function to be solely determined by the manager based upon the requirements of the hotel:
- Incorporates safe work practices in job performance.
- Perform other duties and handle projects as assigned by Manager
### WORK ENVIRONMENT
- Must be able to work effectively in a stressful environment, communicate with others, effectively deal with customers and accept constructive criticism from peers.
- Must be able to change activity frequently and cope with interruptions.
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Innisbrook Resort, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Innisbrook Resort AI Hiring
Innisbrook Resort has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Palm Harbor, FL, US.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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