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About This Role
Charlotte, NC Full-time $70,000-$95,000 Fast-growing company AI Specialist
About Flygreen
Looking for a career with purpose? Flygreen is a leading private jet charter brokerage, focused on delivering our clients a world-class service through our cutting-edge technology and deep expertise. Our dedication to excellence sets us apart in the industry. We ensure every client is paired with the perfect aircraft solution, providing a seamless and memorable experience. We're seeking passionate, driven individuals to join our team and help shape the future of private aviation. If you're passionate about AI and aviation, Flygreen is where you belong.
The Role: Become Flygreen's In-House AI Champion
Are you the person who tries every new model the day it drops, builds side projects with ChatGPT or Claude just to see what's possible, and can't stop thinking about how AI could make everyday work 10x better? We want you.
As Flygreen's AI Specialist, you'll lead our AI work and adoption. You'll spot where AI can make the biggest difference, prototype and ship AI tools and workflows, and partner with our developers to bring them into Flygreen's systems and production, from the first experiment to tools people actually use every day.
This isn't a role where you watch from the sidelines. You'll build real AI tools for our sales, operations, marketing, and client service teams in both Charlotte and Montreal, see the impact right away, and grow fast alongside a growing company. You don't need a decade of experience. You need curiosity, a builder's mindset, and the drive to learn something new every week. We'll give you real ownership, real problems, and a seat in our Charlotte office, working with the people you're building for in both Charlotte and Montreal.
What You'll Do
- Build and ship AI workflows and automations with tools like Grok Bot, ChatGPT, Claude, Gemini, their APIs, and no-code or low-code platforms (Zapier, Make, n8n, or similar):
- Sales: speed up lead intake and qualification, draft quotes and follow-ups, and keep our CRM tidy.
- Operations: help advisors gather and compare aircraft options and operator quotes faster, and automate repetitive trip paperwork.
- Marketing: create on-brand content for social, email, web, and LinkedIn faster, always with human review.
- Client service: prototype and improve AI assistants and message drafting that hand off smoothly to our 24/7 human advisors.
- Partner with our developers. Work with Otto and Alex, our Montreal tech team, to integrate AI into Flygreen's systems: share repos, take part in code reviews, plan how prototypes become production features, and follow the team's engineering practices.
- Evaluate AI tools. Test new products and models, compare them honestly, and recommend what's worth adopting, and what isn't.
- Be the AI champion. Train and support teammates in Charlotte and Montreal, run quick demos and lunch-and-learns, and build simple guides and prompt libraries so everyone gets better with AI.
- Stay on the cutting edge. Track new AI releases and trends, and bring the best ideas back to the team.
- Measure what works. Track time saved and results, and iterate quickly based on feedback.
- Handle data responsibly. With guidance, follow Flygreen's privacy and security practices. Our clients expect discretion, so you'll keep client data safe in every tool you build.
What You Bring
- Hands-on experience building with LLMs or AI APIs (e.g., OpenAI/ChatGPT, Anthropic/Claude, Google Gemini), whether at work, at hackathons, or in personal projects. Show us what you've made!
- 0-3 years of professional experience, or equivalent hands-on projects. Career changers and self-taught builders are welcome.
- Genuine curiosity about AI and a love of learning quickly and experimenting.
- Clear communication and teamwork: you can explain AI to non-technical teammates in plain language, collaborate well with developers, and enjoy helping people succeed.
- Ownership and follow-through: you like taking an idea from "what if?" to "it's live."
- Ability to work full-time on site at our Charlotte office.
Nice to Have
- Hackathons, a GitHub profile, or a portfolio of personal AI projects, agents, bots, or automations.
- Gaming and tech-culture enthusiast: you might have modded a game, built a Discord bot, or streamed. We love people who tinker.
- Experience with prompt engineering, retrieval-augmented generation (RAG), or simple agents.
- Interest in aviation, travel, or luxury hospitality.
What We Offer
- Competitive salary: $70,000 - $95,000 USD base + opportunity for bonuses.
- Opportunity to develop professionally within a growing company.
- Comprehensive benefits package: medical, dental, vision, 401k (generous employer match), unlimited approved PTO, gym membership, etc.
- Opportunity to join a dynamic company with likeminded individuals.
Company Culture
- We are building a team of winners. We are hard-working and determined to succeed!
- We offer a welcoming, fun, and enjoyable team to belong to.
- We have a passion for travel and aviation.
- We host seasonal events and spontaneous team outings.
Salary Context
This $70K-$95K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Flygreen, 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. This role's midpoint ($82K) sits 61% below the category median. Disclosed range: $70K to $95K.
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.
Flygreen AI Hiring
Flygreen has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Charlotte, NC, US. Compensation range: $95K - $95K.
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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