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About This Role
POSITION TITLE: Facilities Maintenance Coordinator
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DIVISION: Recreation \& Facilities
REPORTS TO: Recreation \& Facilities Assistant Director
FLSA STATUS: Full\-time; Non\-Exempt
PAY RANGE: Starting at $21 per hour
Purpose of Position:
This full\-time position is responsible for facilities maintained by Carmel Clay Parks \& Recreation, including but not limited to: Monon Community Center, Indoor Aquatic Center, Waterpark, Splash Pads, and Program Pavilions. This position will have a high level of aquatic facility maintenance responsibilities during the summer season and building maintenance responsibilities during the remainder of the year.
Essential Job Functions:
*The following duties are normal for this position. These are not to be construed as exclusive or all\-inclusive. Other duties may be required and assigned.*
- Monitor general mechanical, electrical, and plumbing operations of the aquatic \& building facilities during scheduled shift.
- Maintain cleanliness and general operations of Indoor Aquatics, Waterpark grounds, splash pads, and CCPR facilities.
- Coordinate daily work activities by prioritizing and monitoring status of work in progress, promptly responding to reported issues, and troubleshooting problem situations.
- Facilitate major projects, including opening and closing of the Waterpark and splash pads and responding to emergency situations that may arise.
- During Waterpark season, work a rotating schedule that includes nights, weekends, and on\-call shifts.
- Ensure compliance with applicable codes, laws, rules, regulations, standards, policies and procedures relating to aquatic \& facility maintenance and visitor or staff safety. Recommend or initiate actions necessary to correct deviations or violations.
- Monitor, record and review water quality readings from pools and splash pads.
- Ensure proper routine maintenance is being conducted on all aquatic equipment including pool filters, chemical feeders, pumps and motors.
- Utilize precautionary safety equipment while performing daily facilities maintenance tasks.
- Assist in hiring, training, and directing general operations of part\-time aquatic staff.
- Assist in managing part\-time staff schedules and makes changes as necessary to ensure that all shifts are covered.
- Attend all required staff trainings and meetings.
- Monitor inventory of equipment and supplies in assigned area; initiate requests for new or replacement materials; research equipment for potential purchase.
- Follow budgeting and purchasing procedures, including reviewing and recommending purchase of supplies.
- Complete all required reports and paperwork in a required timeframe.
- Enforce facility and park rules to guests.
- Respond to customer questions and complaints related to aquatics \& operations and helps to facilitate resolutions.
Knowledge, Skills \& Abilities Required to Perform Essential Job Functions:
- Strong ability to think critically and problem solve building and maintenance issues in a proactive manner.
- Ability to work well independently and as part of a team.
- Ability to work in adverse environmental conditions, such as confined spaces, dirt, dust, pollens, odors, wetness, humidity, rain, fumes, temperature and noise extremes, machinery, vibrations, traffic hazards, toxic agents, disease or pathogenic substances.
- Knowledge in maintenance of pool pumps, filters, chemical controllers, Pulsar units, UV equipment, heaters, Dectron/HVAC units and other equipment deemed necessary.
- Utilize computer and mobile devices to monitor work order software, utilize purchasing system, develop online forms, and various other web\-based systems.
- Competent in use of Microsoft Office, such as Outlook, Word, Excel, Power Point, etc.
- Ability to exert heavy physical effort to perform certain park stewardship tasks, involving a combination of standing, walking, climbing, balancing, kneeling, crouching, lifting, carrying, swimming, pushing and/or pulling of objects and materials weighing 20\-90 pounds and be able to handle heavier loads, as much as 75\-100\+ pounds occasionally.
- Ability to give and understand oral and written instructions.
- Ability to pass a respirator test for use of a respirator for longer than 5 minutes.
Qualifications Required to Perform Essential Job Functions:
- Minimum 18 years of age with high school diploma or GED.
- Minimum of two (2\) years related experience involving aquatic or facilities’ maintenance or any equivalent combination of education, training and experience that provides the requisite knowledge, skills and abilities for this position.
- Driving is an essential function of this position and individual must possess and maintain a valid Indiana driver’s license and be insurable.
- Aquatic Facility Operator (AFO) or Certified Pool Operator (CPO) Certification required within 6 months of hire.
- Confined Space training within six (6\) months of employment.
- Pass a background check that encompasses national, state and local searches throughout employment.
- Pass a pre\-employment drug test.
*The Carmel/Clay Board of Parks and Recreation is an Equal Opportunity Employer. Qualified applicants will receive consideration for employment without unlawful discrimination based on race, color, religion, national origin, gender, disability, sexual orientation, gender identity or expression, family or marital status, ancestry, age, veteran status, or any other legally protected status*.
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 Carmel Clay Parks & Recreation, 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.
Carmel Clay Parks & Recreation AI Hiring
Carmel Clay Parks & Recreation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Carmel, IN, 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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