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About This Role
Overview: Job Title: Principal Artificial Intelligence/ Cloud EngineerLocation: Cherry Point, North Carolina
Rivet Operations Company is looking for a Principal Artificial Intelligence (AI)/ Cloud Engineer to serve as the principal engineer for all software, AI/machine learning (ML), and cloud efforts at Marine Corps Air Station Cherry Point, North Carolina. This role owns the technical architecture for AI/ML and cloud systems and drives the modernization of legacy applications into secure, cloud-based solutions that improve aircraft readiness.
The Principal AI/Cloud Engineer is responsible for defining, documenting, and leading the overall technical architecture for all Aircraft Logistics Department (ALD) software, AI/ML, and cloud systems, and for establishing and enforcing coding standards, development methodologies, and quality assurance processes. This role leads the Risk Management Framework (RMF) and Authorization to Operate (ATO) processes for all developed systems and serves as the primary technical point of contact for government stakeholders. Key Responsibilities* Define, document, and lead the overall technical architecture for all ALD software, AI/ML, and cloud systems, and establish and enforce coding standards, development methodologies, and QA processes.
- Design, develop, train, and continuously improve machine learning models for predictive maintenance, and perform advanced data engineering and feature extraction on large-scale datasets.
- Architect, deploy, and maintain secure applications and infrastructure on government-owned infrastructure.
- Design and implement CI/CD pipelines, containerization, and infrastructure-as-code.
- Integrate language models into existing software
- Enforce data privacy and conduct vulnerability patching when required, in support of the RMF.
- Ensure AI tools and techniques are authorized under DoD operational security (OPSEC) policy and standards.
Requirements: Required Qualifications* U.S. citizenship and ability to maintain a Secret security clearance.
- Experience to meet one of these qualifications is desired:
- A minimum of 7 years’ general experience with an associate’s degree.
- A minimum of 8 years’ general experience with a bachelor’s degree.
- A minimum of 6 years’ general experience with a master’s degree.
In lieu of a degree, candidates may qualify with an additional number of years of relevant experience.* Demonstrated experience designing, building, testing, and deploying machine learning models and AI applications.
- Proficiency with programming and query languages (Python, SQL) and ML/AI frameworks (TensorFlow, PyTorch, or similar).
- Experience designing, building, deploying, and maintaining AI systems and cloud environments on platforms such as AWS, GCP, or Microsoft Azure.
- Experience automating cloud, infrastructure, data, and AI processes; supporting security compliance for AI, cloud, and virtualized environments.
- Experience mentoring junior technical staff.
- Proficiency in Microsoft Office Suite.
- Excellent verbal and written communication skills.
Benefits: Rivet offers a comprehensive benefits package including a liberal vacation plan, a matching retirement program, and competitive salaries commensurate with skills and experience. For more information about our organization, please visit our web site at www.rivetopsllc.com. Physical Requirements: Work may involve sitting or standing for extended periods of time. Position may require typing and reading from a computer screen. Must have sufficient mobility, including but not limited to bending, reaching, and kneeling to complete daily duties in a timely and efficient manner. There is a possibility that due to parking availability and location of work, walking moderate to long distance may be required. Possible lifting up to 40 lbs.
Please note: Rivet Operations Company LLC reserves the right to change or modify job duties and assignments at any time. The above job description is not all encompassing. Positions functions and qualifications may vary depending on business needs.
Rivet Operations Company LLC is an equal opportunity employer and does not discriminate against applicants based on race, color, creed, religion, medical condition, legally protected genetic information, national origin, sex (including pregnancy, childbirth or related medical condition), sexual orientation, gender identity and expression, age, disability, or Vietnam era, or other eligible veteran status or legally protected characteristics. Posted Salary Range: USD $126,000.00 - USD $136,000.00 /Yr.
Salary Context
This $126K-$136K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Rivet, 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($131K) sits 38% below the category median. Disclosed range: $126K to $136K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Rivet AI Hiring
Rivet has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Cherry Point, NC, US. Compensation range: $136K - $136K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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