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About This Role
Charlotte County Airport Authority (CCAA) is currently accepting applications for the full-time, exempt position of Air Service Development Manager at Punta Gorda Airport (PGD). Come join our team and enjoy a great work culture and excellent benefits package including $0 cost to employee Medical, Dental, and Vision plans, cost-free employee health centers, over 16% combined employer contribution to pension and/or investment plans, accrued paid time off, longevity bonuses, and more!
*The following statements are intended to describe the general nature and level of work being performed. They are not intended to be construed as an exhaustive list of all responsibilities, duties and skills required of personnel classified in this job title.*
SUMMARY
Under the direction of the Chief Marketing Officer, the Air Service Development Manager leads Punta Gorda Airport's air service growth strategy by engaging airline partners, developing data-driven route business cases, and analyzing passenger demand, revenue potential, and competitive dynamics. This role serves as the primary point of contact with airline network planners and aviation industry executives. This position requires a strong understanding of airline economics and network planning, fleet and scheduling strategies, and data-driven market analysis along with the ability to translate complex data into clear, persuasive presentations. Frequent domestic travel and occasional international travel are required to represent PGD at airline meetings, conferences, and industry events in support of the Airport's long-term growth objectives.
DUTIES AND RESPONSIBILITIES
Air Service DataMarket Analysis
- Develop and present airline route business cases, including passenger demand forecasts, revenue projections, and competitive and leakage analysis.
- Gather, analyze, and interpret aviation data from sources such as BTS, RITA, O&D data, fare data, passenger forecasts, and capacity metrics.
- Evaluate airline schedules, fleet strategies, and network performance to identify opportunities for new or expanded air service.
- Apply quantitative and statistical analysis to assess market demand, fare trends, and financial impacts for airline and airport partners.
- Prepare clear, data-driven reports and presentations for airline network planning meetings, conferences, and internal leadership.
- Monitor and report key performance indicators such as departures, seats, fares, passenger volumes, and market performance.
- Research peer and competitor airports to inform air service development strategy and recommendations.
AirlineStakeholder Engagement
- Serve as the primary liaison for recruiting new airlines and expanding service with existing carriers.
- Build and maintain strong working relationships with airline executives, network planners, and aviation industry partners.
- Establish and maintain partnerships with economic development organizations, chambers of commerce, travel organizations, and government agencies.
- Represent PGD at airline meetings, air service development conferences, and aviation industry events, including frequent domestic and occasional international travel.
- Deliver targeted, data-driven presentations and pitches to airline decision-makers.
Air Service Strategy, Incentives, and Planning
- Develop annual air service development goals and long-term strategies to support sustainable service growth.
- Manage PGD's Air Carrier Incentive Program (ACIP), including airline applications and compliance with FAA policies.
- Lead negotiations on air service agreements and related contractual arrangements.
- Monitor performance of air service initiatives and recommend adjustments based on market conditions and industry trends.
- Coordinate with airport leadership, staff, consultants, and contractors on air service development initiatives.
Marketing, Community Engagement, and Events
- Collaborate with airlines and destination marketing partners to develop and execute air service marketing initiatives.
- Support promotional efforts tied to ACIP agreements, route launches, and terminal-based marketing.
- Participate in local and regional meetings related to air service and economic development.
- Plan and support airline-related events such as inaugural flights, new route announcements, and service milestones.
- Host airline executives, industry partners, and stakeholders during airport visits and tours.
Financial and Regulatory Coordination
- Manage budgets related to air service development initiatives, travel, and marketing activities.
- Provide recommendations related to airline rates, charges, and air passenger operations based on market and financial analysis.
- Coordinate with finance staff to support forecasting and budgeting related to proposed or planned air service changes.
- Ensure air service initiatives and incentive programs comply with FAA policies and applicable federal, state, and local regulations.
- Assist airport leadership with regulatory coordination, funding justifications, and agency discussions related to air service development.
- Performs other duties as assigned or required.
MINIMUM JOB REQUIREMENTS
- Bachelor's degree from an accredited college or university in aviation, business, economics, finance, marketing, or a related field; or an equivalent combination of education and progressively responsible, directly related experience.
- Four (4) years of professional-level experience in air service development, airline network planning, aviation analysis, airport management, transportation analytics, applied economics, or data analytics, or a closely related field.
- Ability to travel frequently within the United States and occasionally internationally.
- Valid Florida Driver's License.
KNOWLEDGE, SKILLS, AND ABILITIES
- Experience analyzing aviation and market data, including sources such as BTS, RITA, O&D data, fare data, passenger forecasts, and capacity metrics, or comparable large-scale datasets used for demand, revenue, or market analysis.
- Experience applying data analytics, applied economics, or quantitative analysis to support business cases, forecasting, or strategic decision-making in aviation, transportation, travel, or related industries.
- Knowledge of airline economics, fleet and scheduling strategies, and network planning dynamics, with the ability to evaluate market demand, fare trends, and financial impacts using quantitative and statistical techniques.
- Knowledge of FAA policies related to air carrier incentives, airport certification, and airspace considerations.
- Strong written and verbal communication skills, including the ability to prepare and deliver clear, persuasive presentations to executive-level audiences.
- Ability to build and maintain effective working relationships with airlines, government partners, economic development organizations, and travel industry stakeholders.
- Experience negotiating agreements or contracts related to air service or aviation business development.
- Strong organizational and project management skills, with the ability to manage multiple priorities and deadlines.
- Proficiency with Microsoft Office applications, including Word, Excel, PowerPoint, and data presentation tools.
WORKING CONDITIONS AND PHYSICAL EFFORT
- Work is primarily performed in an office and professional meeting environment.
- Frequent domestic travel and occasional international travel are required to attend airline meetings, conferences, and industry events.
- The position may require attendance at meetings or events outside of normal business hours.
- Work involves frequent use of a computer, phone, and standard office equipment.
- Occasional exposure to airport operational areas and varying weather conditions may be required.
- The physical demands and work environment described are representative of those required to perform the essential functions of the position, with or without reasonable accommodation.
Equal Opportunity Employment
The Charlotte County Airport Authority (CCAA) is an equal opportunity employer. We do not discriminate based on race, color, national origin, sex (including pregnancy, gender identity, and sexual orientation), religion, veteran status, age, disability, or genetic information.
The Charlotte County Airport Authority is a Veterans' Preference employer; eligible veterans are encouraged to apply in accordance with Florida law (Section 295, Florida Statutes). CCAA is also proud to be a Tobacco-Free/Drug-Free Workplace.
Reasonable Accommodation Statement
The Charlotte County Airport Authority (CCAA) is committed to providing reasonable accommodations to individuals with disabilities during the application, interview, and employment processes, in compliance with federal and state laws.
If you require a reasonable accommodation to participate in any part of the employment process, including the application or interview, or to perform essential job functions, please contact Human Resources at HR@FlyPGD.com.
Salary Context
This $86K-$109K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $170K across 217 roles with salary data).
View full AI/ML Engineer salary data →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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Charlotte County Airport Authority, 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 $154,000 based on 8,743 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $147,000. This role's midpoint ($97K) sits 36% below the category median. Disclosed range: $86K to $109K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Charlotte County Airport Authority AI Hiring
Charlotte County Airport Authority has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Punta Gorda, FL, US. Compensation range: $109K - $109K.
Location Context
Across all AI roles, 7% (2,732 positions) offer remote work, while 34,484 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $145,600. 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 (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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