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About This Role
nLogic is seeking a Principal AI/Cloud Engineer to support anticipated work on a mission-focused effort in the Cherry Point area. This role will play a key part in leading artificial intelligence, machine learning, cloud architecture, automation, and systems modernization initiatives within a collaborative defense and technology environment.
In this role, you will provide technical leadership for AI and cloud engineering efforts, guiding the design, deployment, and sustainment of scalable solutions that support mission-critical operations and enterprise modernization objectives.
Responsibilities
- Lead the design, development, testing, and deployment of AI and machine learning solutions
- Architect and implement cloud-based environments supporting AI and enterprise applications
- Provide technical leadership for AI and cloud engineering initiatives
- Develop production-ready artificial intelligence systems.Lead cloud infrastructure deployment and modernization activities
- Support migration of on-premises systems to cloud environments
- Design and implement automation solutions that improve operational efficiency and scalability
- Support compliance with security requirements and industry best practices
- Monitor AI model, application, and cloud environment performance
- Provide technical guidance and troubleshooting support across engineering teams.
Required Qualifications
- Active, in-scope Secret security clearance or the ability to obtain and maintain a Secret security clearance required.
- Education and Experience + Bachelor's degree in a STEM-related field and 8 years of relevant experience; or Master's degree in a STEM-related field and 6 years of relevant experience; or PhD in a STEM-related field and 4 years of relevant experience; or Associate degree and 7 years of relevant experience; or High school diploma and 12 years of relevant experience
- Experience designing, building, testing, and deploying AI and machine learning solutions
- Experience integrating software engineering and data science principles
- Experience with Python, SQL, and associated development tools
- Experience with TensorFlow, PyTorch, or similar AI frameworks
- Experience designing and deploying cloud architectures within AWS, Azure, GCP, or equivalent platforms
- Experience maintaining cloud and virtualized infrastructure environments
- Experience supporting cloud migration efforts
- Experience automating infrastructure, cloud, data, and AI processes
- Experience supporting security compliance initiatives
- Experience monitoring system, application, AI model, and cloud performance
Preferred Qualifications
- Experience leading enterprise AI and cloud initiatives
- Experience supporting large-scale cloud modernization efforts
- Relevant cloud, AI, or cybersecurity certifications
- Experience supporting mission-critical DoD systems
- Experience providing technical leadership across multidisciplinary teams
Position Details
- Work Model: Full-Time, Onsite in Cherry Point, NC
- Travel: Occasional travel may be required in support of customer requirements
- Work Hours: Standard work hours
- U.S. Citizenship is required
- Active U.S. Secret security clearance required (or ability to obtain and maintain)
Why *n*Logic
At *n*Logic, we support mission-focused defense and space efforts that have real-world impact. Our teams work collaboratively alongside government and industry partners to deliver high-quality, mission-focused solutions.
As a 100% employee-owned company (ESOP), our employees directly share in the success of the organization, creating a culture of accountability, ownership, and long-term investment in our work.
We offer a stable environment with opportunities to develop new skills, take on meaningful responsibilities, and contribute to efforts that support national security and advanced technologies.
Summary of Benefits
*n*Logic offers a comprehensive benefits package including medical, dental, and vision insurance; paid time off; 401(k) retirement plan; Employee Stock Ownership Plan (ESOP); and professional development support.
How to Apply
Qualified candidates should submit their application through our online portal to be considered for this opportunity.
Equal Opportunity Employer
*n*Logic is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.
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 nLogic, 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. Senior-level AI roles across all categories have a median of $229,300.
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.
nLogic AI Hiring
nLogic has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Cherry Point, NC, US.
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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