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About This Role
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 :
Senior AI Engineer – Agentic Solutions \& Digital Thread
Reporting To:
Management, IT Applications Development
Work Schedule:
Hybrid – Buffalo, NY
Moog's Corporate Group is looking for a Senior AI Engineer – Agentic Solutions \& Digital Thread to join our Team!
The Senior AI Engineer – Agentic Solutions \& Digital Thread is an experienced technologist who designs, builds, operates, and continuously improves enterprise AI\-enabled applications. This role emphasizes agentic architectures, advanced Retrieval\-Augmented Generation (RAG), and intelligent automation within a complex, multi\-site aerospace and defense manufacturing environment subject to ITAR/EAR, CMMC/DFARS, and rigorous quality standards.
The role serves as the lead developer for assigned AI and agentic solution use cases, translating business requirements into scalable, auditable, and explainable implementations aligned to enterprise Digital Thread, Master Data Management (MDM), Intelligent Automation and Smart Factory initiatives. This role applies advanced knowledge acquired through relevant and substantial work experience. As the lead developer, this role is also comfortable experimenting with new techniques and coaching other development staff on AI\-assisted and Agentic software delivery practices. This role assists others with more complex problems and designs elegant solutions to tackle them.
To qualify for the Senior AI Engineer – Agentic Solutions \& Digital Thread role, here is what we would expect you to bring to Moog...
- Typically, a bachelor’s degree in computer science, Software Engineering, AI/ML, or equivalent experience.
- 8\+ years of professional software development, with 3\+ years focused on AI/ML solution development.
- Hands\-on experience with agentic AI frameworks (Semantic Kernel, AutoGen, LangChain/LangGraph, CrewAI, or equivalent), MCP (Model Context Protocol), multi\-agent orchestration, and secure tool integration patterns.
- Demonstrated experience designing and implementing advanced RAG architectures, including vector databases, embedding models, retrieval strategies, and Context Engineering techniques to ground AI responses in enterprise knowledge.
- Strong communication and collaboration skills with technical and non\-technical stakeholders.
- Proficiency in Python and at least one additional language (C\#, .NET, or TypeScript).
- Strong experience with Microsoft Azure services and cloud\-native architectures.
- Experience with CI/CD pipelines, Infrastructure as Code, automated testing, and Agile/DevOps delivery.
- Experience with Databricks, lakehouse architectures, and enterprise data integration patterns.
- Experience with event\-driven architectures and integration platforms (Azure Service Bus, Event Grid, Kafka).
- Exposure to MDM, data governance, data quality frameworks, or rules\-based systems.
- Exposure to digital thread concepts in manufacturing, including PLM (Teamcenter), ERP (SAP), MES, and QMS integration.
- Experience in aerospace, defense, or regulated manufacturing environments (ITAR, EAR, CMMC, DFARS).
- Familiarity with AI governance frameworks (e.g., NIST AI RMF) and responsible AI practices.
- Azure certifications (Azure AI Engineer, Azure Solutions Architect) are a plus.
As the Senior AI Engineer – Agentic Solutions \& Digital Thread, you will...
- Design and implement agentic software architectures where AI agents perform code generation, test automation, and documentation, always under human oversight with deterministic guardrails and clear accountability boundaries.
- Build agent workflows that exercise rules management, auditability, deterministic logic, high trust thresholds, and reusability across domains, using MDM and data quality platforms as foundational workloads, with fallback\-to\-human protocols for safety\-critical decisions.
- Design and implement advanced RAG architectures that ground AI responses in authoritative enterprise data.
- Build and maintain vector stores, embedding pipelines, and hybrid retrieval strategies optimized for domain\-specific technical content.
- Enforce data classification, export control boundaries, and role\-based access controls.
- Develop event\-driven integrations aligned to digital thread principles.
- Build AI\-enabled capabilities that consume digital thread events to automate downstream decision\-making and exception detection.
- Implement thread contracts to ensure deterministic, auditable system behavior that eliminates manual reconciliation.
- Design and maintain AI\-enabled applications using Python, C\#/.NET on Microsoft Azure, leveraging Azure OpenAI, Claude, Azure AI Search, Databricks, and agent frameworks (Semantic Kernel, AutoGen, LangChain, or equivalent).
- Implement CI/CD pipelines, Infrastructure as Code, automated testing, monitoring, telemetry, and operational runbooks for production\-grade reliability.
- Implement rule\-level execution logs, record\-level lineage, match/merge traceability, and interface health dashboards with explainability layers for state changes and agent decisions.
- Ensure embedded governance\-by\-design and compliance with ITAR/EAR, CMMC/DFARS, and CUI handling requirements.
- Collaborate with product owners, enterprise architects, data teams, and business stakeholders.
- Coach and review the work of junior developers.
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 $135,000\.00–$155,000\.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 $135K-$155K 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 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($145K) sits 33% below the category median. Disclosed range: $135K to $155K.
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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