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About This Role
Mission-Driven. Honor-Bound. Fairbanks Morse Defense. What’s your calling? For the men and women of Fairbanks Morse Defense it’s the rapid and effective support of the fleets that entrust us to design, develop, and deliver the best naval power technology on the planet. We’re one of the nation’s leading marine defense contractors because of the highly trained and dedicated teams that provide reliable power systems, parts, and aftermarket services to the U.S. Navy, Coast Guard, Military Sealift Command, and Canadian Coast Guard. When you join Fairbanks Morse Defense, you join a legacy of commitment that spans 150 years. Fairbanks Morse Defense offers competitive benefits with a wide range of training and development opportunities. Our world-class teams are dedicated to helping every individual achieve their full potential. Discover what it’s like to be a part of the Fairbanks Morse Defense team here: https://www.fairbanksmorsedefense.com/careers
Summary
Fairbanks Morse Defense builds and supports the engines and power systems that keep U.S. Navy and Coast Guard ships underway. Our Aftermarket business is launching a portfolio of AI, business intelligence, and process-automation projects under the Fairbanks Morse Operating System (FMOS) continuous improvement program, with one goal: help our people quote faster, serve customers better, and spend less time on manual data work.
We are hiring an Applied AI Engineer to be the hands-on builder behind that portfolio. The idea pipeline is already prioritized and the program has active executive sponsorship. What we need is the engineer who turns those ideas into secure, production-grade AI solutions running in Microsoft Azure: an inside-sales assistant that answers questions from our technical manuals, an agent that reviews contracts and drafts quotes, automated pricing and PO checks, onboarding assistants for new hires, and more.
This is a fully remote role. No security clearance is required.
Why This Role
- Greenfield work with real users. You will design and ship from a prioritized backlog, not maintain someone else’s legacy system, and see your work used by sales, service, and engineering teams.
- Modern tools, encouraged. We expect you to build with AI-assisted coding tools such as Claude Code, Cursor, GitHub Copilot, or Codex, and to help set the standard for using them securely.
- Visible impact. You will work directly with executive sponsors and business leaders, and your results will be measured in hours saved, faster quotes, and better customer service.
- A mission that matters. Your solutions help keep U.S. naval vessels ready and supported.
Principal Duties and Responsibilities
- AI Solution Development & Deployment
- Partner with the continuous improvement lead, executive sponsors, and business subject-matter experts to turn prioritized ideas into deployed, measurable solutions, starting with the highest-impact, lowest-effort items.
- Design, build, and deploy AI agents using retrieval-augmented generation (RAG), tool use / function calling, and human-in-the-loop controls for use cases such as a technical-data inside-sales assistant, contract review and quoting, project-brief and after-action-review generation, and new-hire onboarding.
- Build document-processing and OCR workflows over technical manuals, contracts, Excel workbooks, and scanned content.
- Develop process-automation solutions, such as EDI-style order processing, PO review and compliance checks, price justification, and “like-parts” categorization, that remove manual effort and improve accuracy.
- Use AI-assisted coding tools to accelerate development and help establish best practices for their secure use across the team.
- Azure & Platform
- Deploy and operate solutions in Microsoft Azure, including App Service / Functions, storage, identity, and Azure OpenAI, within a government-cloud (GCC-High) tenant.
- Work with the IT team to connect solutions to existing systems through virtual networks, private endpoints, and secure APIs.
- Set up CI/CD pipelines, logging, and monitoring so solutions are repeatable, observable, and reliable in production.
- Handle credentials, model inputs/outputs, and sensitive data securely, in line with the company’s government-contract compliance requirements.
- Integration & Data
- Integrate with enterprise systems such as Oracle ERP, Salesforce, Oracle Hyperion Financial Management (HFM), and CPQ / quoting tools; build and secure the REST APIs and data pipelines that feed AI solutions.
- Support reporting and dashboard needs (e.g., revenue/margin, P&L, and utilization) where they complement AI and automation projects.
- Technical Leadership
- Serve as the technical lead for AI and automation projects: help define requirements with subject-matter experts and coordinate with FMD’s technology development and AI team and with IT.
- Establish development standards, documentation, and evaluation / observability practices to measure and continuously improve AI output quality.
- Help track solution impact and ROI so the program can demonstrate value early.
- Provide technical direction to other developers and contract resources supporting the initiative.
- Other duties as assigned.
Required Qualifications
- 5+ years building and supporting production software, automation, or AI solutions, with a BS in Computer Science, Software Engineering, or a related field, or equivalent practical experience.
- Hands-on experience deploying and operating applications in Microsoft Azure (e.g., App Service / Functions, storage, identity, and Azure OpenAI / Azure AI services).
- Demonstrated experience shipping LLM-based applications to production, including retrieval-augmented generation (RAG), tool use / function calling, and orchestration.
- Strong Python and SQL skills, plus REST API design and integration using modern authentication (OAuth 2.0 / OpenID Connect).
- Working experience with Git, CI/CD, and containerization (e.g., Docker), and with AI-assisted coding tools such as Claude / Claude Code, Cursor, Codex, Gemini, or GitHub Copilot.
- U.S. citizenship. This position requires access to Unclassified Naval Nuclear Propulsion Information, which is Not Releasable to Foreign Nationals (NOFORN). Candidates must provide proof of U.S. citizenship (U.S. passport, U.S. birth certificate, or naturalization certificate) and attest that they do not hold dual citizenship. No security clearance is required.
Preferred Qualifications
- Experience building applications in Azure Government Cloud (GCC-High, FedRAMP, IL4 / IL5) environments.
- Experience with CMMC and government-contract IT compliance, including NIST 800-171, DFARS, STIG, and RMF.
- Network design and integration experience, including virtual networks, VPN / private endpoints, and secure connectivity.
- Experience with agent frameworks such as Microsoft Semantic Kernel, LangChain, LangGraph, or AutoGen, and with vector databases, embedding models, and RAG over large technical-document collections.
- Familiarity with LLM evaluation, observability, and prompt-engineering best practices.
- Experience with EDI or order-processing automation, and with the Microsoft Power Platform (Power Automate, Power Apps).
- Experience building BI dashboards and data models (e.g., Power BI) from ERP and CRM systems such as Oracle and Salesforce.
- Experience integrating with Oracle ERP, Salesforce, HFM, and CPQ / quoting platforms.
- JavaScript / TypeScript experience; C# / .NET is a plus.
- Infrastructure-as-code (Terraform, Bicep) and CI/CD with Azure DevOps or GitHub Actions.
- Product ownership experience, such as managing a backlog, defining requirements, and tracking ROI.
- Experience in manufacturing, defense, or maritime / aftermarket service environments, and familiarity with Lean / Six Sigma or other continuous-improvement methods.
This specification is intended to indicate the kinds of tasks and levels of work difficulty that will be required of positions that will be given this title and shall not be construed as declaring what the specific duties and responsibilities of any particular position shall be. It is not intended to limit or in any way modify the right of any supervisor to assign, direct, and control the work of employees under his/her supervision. Ability to competently perform all the essential duties of the position, with or without reasonable accommodation, demonstrated commitment to effective customer service delivery, and the ability to work productively as a member of a team or work group are basic requirements of all positions at Fairbanks Morse Defense.
Fairbanks Morse Defense offers a competitive benefits package including but not limited to, medical, dental, vision, life, and disability insurances, and 401k retirement plan with employer match. In addition, we offer paid time off, paid holidays, paid parental leave, and tuition reimbursement.
Equal Opportunity Employer/Veterans/Disabled
Salary Context
This $113K-$169K range is below the median 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 Fairbanks Morse Defense, 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($141K) sits 33% below the category median. Disclosed range: $113K to $169K.
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.
Fairbanks Morse Defense AI Hiring
Fairbanks Morse Defense has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, Fairfield, OH, US. Compensation range: $169K - $169K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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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