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About This Role
- Req Number: 40411
- Location: Columbus, OH
- Postal Code: 43219
- Country: United States
- Posted Date: 7/13/2026
About FlightSafety International
FlightSafety International is the world’s premier professional aviation training company and supplier of flight simulators, visual systems and displays to commercial, government and military organizations. The company provides training for pilots, technicians and other aviation professionals from 167 countries and independent territories. FlightSafety operates the world’s largest fleet of advanced full\-flight simulators and award\-winning maintenance training at Learning Centers and training locations in the United States, Canada, France and the United Kingdom.
Purpose of Position
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The VP, AI Transformation is responsible for defining, establishing, scaling, and leading AI transformation in a complex, high\-value industry environment, building and scaling enterprise\-wide adoption of AI capabilities from the ground up. This role will define how AI drives competitive advantage across manufacturing, engineering, and aviation training operations, and be accountable for evolving AI from experimentation into enterprise capability, delivering measurable impact across cost structure, operational performance, and product innovation. This role requires a leader with demonstrated experience building foundational capabilities, educating stakeholders, identifying high\-value opportunities, and implementing secure, compliant, and scalable AI solutions for the business. This candidate will balance innovation with risk management while ensuring compliance with defense industry requirements, cybersecurity regulations, global data protection laws, and international operational considerations.
Tasks and Responsibilities
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- Define and execute a multi\-year AI strategy roadmap aligned to business priorities and the modernization of digital initiatives
- Build a value\-backed portfolio of AI initiatives tied to cost reduction, throughput and quality improvements, and revenue enablement
- Establish a roadmap which balances innovation, operational efficiency, risk management, and quantitative value
- Develop a scalable AI operating model (people, process, technology, governance) and partnering with executive leadership to identify \& prioritize the opportunities across all business units
- Partner with finance and establish clear ROI frameworks and accountability for outcomes
- Deliver material EBITDA impact through AI\-driven initiatives
- Stand up and scale an AI Center of Excellence (CoE)
- Lead operational transformation across corporate functions, manufacturing operations, engineering and simulation by incorporating AI into core business workflows and processes building executive \& leadership understanding of AI capabilities, limitations, risks and emerging trends.
- Define the enterprise AI architecture in partnership with IT and executive leadership
- Establish enterprise AI governance, including responsible AI principles, model risk management and data privacy and IP protection
- Partner with Legal, Risk, and Cybersecurity to mitigate emerging threats
- When appropriate, ensure AI initiatives align with defense industry requirements, including:
- CMMC (Cybersecurity Maturity Model Certification)
- NIST SP 800\-171, NIST SP 800\-53
- Controlled Unclassified Information (CUI) handling requirements
- Export control considerations
- Government customer cybersecurity requirements
- Establish secure AI usage standards for environments containing sensitive, regulated, or controlled information.
- Collaborate with cybersecurity, compliance, legal, and contracts teams to evaluate AI\-related risks, contractual obligations, and emerging threats.
- Develop policies governing use of commercial and generative AI platforms in regulated environments
- Drive enterprise\-wide change management and adoption while assessing organizational and system readiness related to data quality, architecture, and governance
- Guide decisions regarding AI cloud services, private environments and enterprise architectures while ensuring the initiatives align with the evolving architecture and cybersecurity standards.
Minimum Education
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- Bachelor's degree from an accredited institution or equivalent industry experience
- Advanced degree and/or certifications in AI preferred
Minimum Experience
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- Seven or more years spearheading the development and execution of AI engineering, software engineering, and/or advanced data science initiatives, with experience in designing and delivering adaptive/intelligent solutions, coordinating AI platforms, and selection/use of AI embedded in enterprise applications.
- 5\+ years leading enterprise\-scale technology transformation programs
- Preferably ten or more years in various adjacent areas. This should include, but not be limited to, software engineering, data engineering \& management, analytics, AI models life cycle management, knowledge engineering, unstructured data \& information management, and familiarity with a wide array of AI techniques.
- Experience developing AI, analytics, automation, or digital transformation strategies and strong understanding of generative AI, machine learning, large language models, automation technologies, and enterprise AI platforms
- Experience operating within regulated industries or government contracting environments preferred
- Proven track record of driving measurable business impact at scale
- Experience in manufacturing, aerospace, aviation, or engineering\-intensive industries strongly preferred
- Demonstrated success building and leading cross\-functional transformation teams
Knowledge, Skills, Abilities
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- Understands how decision intelligence, agentic AI, and other AI practices/approaches/application areas can transform decision making and work practices. Articulate AI's transformative impact on current and future business objectives, including its potential to drive new business models, regenerate existing workflows, optimize operations, and deliver measurable value based on key performance indicators (KPIs).
- Experience integrating AI into complex, legacy\-rich environments.
- Demonstrates the ability to comprehend industry dynamics and the organization's core mission to pinpoint where AI can yield the most significant impact and competitive advantage, while also identifying relevant business risks and opportunities, including how AI can disrupt or extend existing business models.
- Pragmatic and execution\-focused (avoids hype\-driven approaches)
- Builder mindset—comfortable creating capability from zero
- High accountability and bias for results
- Strong commercial acumen—can tie initiatives directly to P\&L impact
- Ability to operate at both the Executive/board level and Hands\-on execution level
- Exceptional stakeholder influence across technical and non\-technical audiences
- Superior analytic and decision\-making skills. Ability to quickly size up where and how the organization creates value, identify opportunities and risks, and develop appropriate, actionable plans
- Expert ability to develop strong business partnerships and influence senior and executive management
- Expert experience managing remote/virtual teams and partners
- Excellent mentoring and team development skills
Physical Demands and Work Environment
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The physical demands and work environment described here are representative of those that must be met and/or encountered by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
While performing the duties of this job, the employee is regularly required to use hands to handle, or feel; reach with hands and arms; and communicate. The employee may be required to stand, walk, and sit.
Specific vision abilities required by this job include the ability to view monitors, technical documents, and reference material.
The noise level in the work environment is usually low to moderate.
FlightSafety is an Equal Opportunity Employer/Vet/Disabled. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or disability.
*Any offer of employment is contingent upon successful completion of required compliance reviews, including verification that the candidate is not prohibited from employment under U.S. economic sanctions programs administered by the U.S. Department of the Treasury’s Office of Foreign Assets Control (OFAC).*
*This position may require access to export\-controlled technology or services subject to the International Traffic in Arms Regulations (ITAR) and/or the Export Administration Regulations (EAR). Employment consideration and any offer of employment are contingent upon the applicant’s ability to comply with these requirements, including qualifying as a “U.S. Person” under applicable regulations or otherwise eligible for export authorization within a timeframe consistent with business needs. A “U.S. Person” includes U.S. citizens, lawful permanent residents (holders of approved and unexpired green cards), and certain refugees or asylees with protected status under U.S. law.*
*This position may also require eligibility to obtain and maintain a U.S. Government security clearance for the duration of employment.*
Cybersecurity Notice: All official recruiting communication from FlightSafety International will come from an @flightsafety.com email address. FlightSafety International will never ask for personal or financial information through social media or third\-party email providers.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At FlightSafety International, 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 $218,750 based on 3,817 positions with disclosed compensation.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
FlightSafety International AI Hiring
FlightSafety International has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Columbus, OH, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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