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Moog is a performance culture that empowers people to achieve great things. Our people enjoy solving interesting technical challenges in a culture where everyone trusts each other to do the right thing. For you, working with us can mean deeper job satisfaction, better rewards, and a great quality of life inside and outside of work.
Job Title :
Corporate AI Security Architect
Reporting To:
Officer, Chief Info Security
Work Schedule:
Hybrid – Buffalo, NY
Moog's Corporate Group is looking for a Corporate AI Security Architect to join our Cyber Security Team!
As the Corporate AI Security Architect, you will lead the development, implementation, and continuous evolution of cyber security architecture and governance for Moog's enterprise Artificial Intelligence ecosystem. You will serve as the organization's primary subject matter expert for AI security, ensuring the secure, compliant, and standardized use of AI technologies, including internally authorized Large Language Models (LLMs) and third\-party platforms such as Microsoft Copilot.
In this role, you will drive enterprise\-wide AI security strategy, define cyber security standards and controls, establish governance frameworks, and partner with Operating Groups, IT, Legal, Compliance, and Executive Leadership. You will play a critical role in enabling responsible AI adoption while protecting Moog's intellectual property, export\-controlled data, and critical business information from emerging AI\-related risks.
Applicants must live local to the Buffalo, NY area, be willing to relocate, and be able to support a hybrid work schedule (3 days in office) at Moog's East Aurora headquarters.
This position requires access to data subject to U.S. government citizenship restrictions.
To qualify for the Corporate AI Security Architect role, here is what we would expect you to bring to Moog…
- Typically, a master's degree in Information Technology, Cyber Security, Computer Science, or a related field
- Ten (10\)\+ years of experience in cyber security architecture with at least two (2\)\+ years focused on AI/ML security
- Demonstrated expertise in AI/ML technologies, including Large Language Models (LLMs), and associated security, governance, and risk considerations
- Deep understanding of AI security threats including prompt injection, jailbreaking, data poisoning, model inversion, and output exfiltration risks
- Direct experience with ITAR and EAR regulations as they apply to technical data, software, and AI/ML systems
- Experience designing, implementing, and certifying regulated cloud environments including FedRAMP, CMMC Level 2, and/or DoD IL4/IL5 environments
- Strong working knowledge of industry frameworks and standards such as NIST CSF, NIST 800\-53, NIST AI Risk Management Framework (AI RMF), ISO 27001, and similar frameworks
- Experience designing and implementing governance, monitoring, and control frameworks within complex enterprise environments
- Experience with Data Loss Prevention (DLP) and information rights management in cloud and AI\-enabled environments
- Experience producing executive\-level reporting, metrics, dashboards, and risk communications
- Strong leadership, communication, and influencing skills with the ability to communicate complex technical concepts to executive and non\-technical audiences
- Proven ability to manage multiple complex priorities independently while maintaining accountability and quality
- Demonstrated ability to serve as a senior technical leader and trusted advisor within a rapidly evolving technology landscape
- Background in adversarial machine learning and AI red\-teaming methodologies preferred
- Experience evaluating third\-party AI vendors, supply chain AI risks, and model security controls preferred
- Professional certifications such as CISSP, CISM, CRISC, or AI/security\-related certifications preferred
As the Corporate AI Security Architect, you will…
- Architect and lead the cyber security strategy, governance, and technical direction for Moog's enterprise AI Program
- Develop, implement, and continuously enhance AI security policies, standards, controls, and enforcement strategies
- Serve as the enterprise subject matter expert for AI security governance and provide guidance on the secure and compliant use of AI technologies
- Own and maintain Moog's AI compliance framework, including requirements supporting ITAR and EAR regulations
- Define standards for permissible data inputs, output monitoring, geographic access controls, model access policies, and audit trail requirements
- Evaluate AI platforms and vendors for security posture, data residency, training data practices, certifications, and export control compliance requirements
- Lead AI security certification initiatives and establish the architecture and documentation required to achieve compliance objectives
- Conduct threat modeling activities and security architecture reviews for AI solutions entering production environments
- Participate in investigations involving AI\-related security incidents, including prompt injection attempts, unauthorized data access, model misuse, and export control concerns
- Establish governance requirements for controlled technical data, CUI, intellectual property, personnel information, and business data used within AI systems
- Develop AI security awareness content and provide guidance to governance committees, leadership teams, and the broader workforce
- Maintain AI security metrics and deliver regular reporting to executive leadership and oversight committees
- Partner with the CISO, Operating Groups, IT, Legal, Compliance, and other functional teams to integrate AI security requirements into business processes and workflows
- Provide strategic direction for AI threat intelligence, vulnerability management, and incident response capabilities
- Lead enterprise AI risk identification and assessment activities, including model risks, prompt injection, data exposure, and third\-party vendor vulnerabilities
- Support enterprise decision\-making regarding AI tool selection, vendor approvals, and secure technology adoption
- Monitor emerging AI threats, technologies, regulatory developments, and industry trends, translating insights into actionable improvements
- Establish and maintain the strategic roadmap for Cyber Security within AI, ensuring alignment with enterprise priorities and business objectives
How We Care for You:
- Financial Rewards: great compensation package, annual profit sharing, matching 401k, and the ability to participate in Employee Stock Purchase Plan, Flexible Spending and Health Savings Accounts
- Work/Life Balance: Flexible paid time off, holidays and parental leave program.
- Health \& Welfare: Comprehensive insurance coverage including medical, dental, vision, life, disability, Employee Assistance Plan (“EAP”) and other supplemental benefit coverages.
- Professional Skills Development: Tuition Assistance, mentorship and coaching opportunities, leadership development and other personal growth programs
- Diverse and Inclusive Workplace: Employee Resource Groups, cultural events, and celebrations.
Salary Range Transparency:
Buffalo, NY $150,000\.00–$190,000\.00 Annually
Salary Range Disclaimer
The base salary range represents the low and high end of the Moog salary range for this position in the given work location. Actual salaries will vary depending on factors including but not limited to location, experience, and performance. The range(s) listed is just one component of Moog's total compensation package for employees. Other rewards may include annual bonuses, employee stock purchase plan, an open paid time off policy, and many region\-specific benefits.
This position requires access to U.S. export\-controlled information.
EOE/AA Minority/Female/Sexual Orientation/Gender Identity/Disability/Veteran
*Moog offers an exclusive workplace, and, as such, affirms the right of every person to participate in all aspects of employment based on merit without regard to race, religion, color, national origin, citizenship, sex, sexual orientation, gender identity, age, veteran status, disability, genetic information, or any other protected characteristic. If you are interested in applying for employment and need special assistance or an accommodation to apply for a posted position, contact our Human Resources department via phone at 844\-367\-5787\.*
No unsolicited agency submittals please. Agency partners must be invited to participate in a search by our Talent Acquisition Team and have signed terms in place prior to any submittal. Absent compliance with these pre\-conditions resumes submitted directly to any Moog Inc. employee or affiliate will not qualify for fee payment, and therefore become the property of Moog Inc.
Salary Context
This $150K-$190K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Moog, Inc, 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($170K) sits 21% below the category median. Disclosed range: $150K to $190K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Moog, Inc AI Hiring
Moog, Inc has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Buffalo, NY, US. Compensation range: $140K - $190K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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