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About This Role
MTSI is seeking an AI/ML Systems Engineering Subject Matter Expert to support ground systems architecture, digital engineering, DevSecOps, and data\-driven decision support. This role combines ground systems architecture expertise with applied AI/ML, agentic workflows, and advanced analytics to improve the speed, quality, traceability, and defensibility of architecture decisions across complex mission systems.
The selected candidate will lead efforts to identify, design, prototype, and integrate AI/ML\-enabled engineering capabilities into ground systems architecture workflows, CI/CD pipelines, technical baseline management, trade studies, and mission engineering processes. This position will work closely with systems engineers, software teams, DevSecOps teams, cybersecurity stakeholders, data engineers, program leadership, and government customers to translate mission and engineering challenges into practical AI\-enabled solutions. Responsibilities:* Lead the application of AI/ML, large language models, agentic workflows, and data analytics to ground systems architecture and systems engineering challenges.
- Develop AI\-enabled approaches for analyzing large engineering datasets, including requirements, architecture artifacts, interface data, test results, operational data, defect trends, and technical documentation.
- Use agentic AI methods to support architecture trade studies, design decision analysis, risk identification, technical baseline assessment, modernization planning, and mission/thread analysis.
- Identify opportunities to improve CI/CD and DevSecOps pipelines through AI/ML\-assisted automation, anomaly detection, test prioritization, quality gates, deployment insights, documentation support, and engineering workflow optimization.
- Lead the development and documentation of the Government Reference Architecture (GRA) for ground segments, ensuring alignment with Air Force strategic goals and objectives.
- Analyze existing and emerging ground segment architectures, technologies, and standards to inform the GRA development process.
- Support ground systems architecture development, interface analysis, system decomposition, requirements traceability, technical reviews, and integration planning.
- Analyze existing and emerging ground segment architectures, technologies, and standards to inform the GRA development process.
- Develop solutions and recommendations to improve data exchange, communication protocols, and functional integration.
- Translate user needs and future platform requirements into the GRA, ensuring alignment with interoperability objectives.
- Develop and deliver comprehensive technical documentation for the GRA, including architectural diagrams, interface specifications, and implementation guidelines.
- Define architectural principles, standards, and guidelines to promote interoperability, modularity, severability, and scalability across future adopting platform ground segments.
- Partner with engineering and software teams to design repeatable, secure, and auditable AI/ML workflows suitable for controlled, or mission\-critical environments.
- Define human\-in\-the\-loop review processes, validation methods, governance controls, and traceability mechanisms for AI\-assisted engineering recommendations.
- Evaluate emerging AI/ML, agentic AI, data engineering, and Machine Learning Operations (MLOps) technologies for applicability to ground systems and digital engineering environments.
- Communicate technical findings, architecture recommendations, AI/ML opportunities, and implementation roadmaps to program leadership and government customers.
- Help establish reusable AI/ML\-enabled systems engineering practices, patterns, and reference architectures across programs.
Qualifications Required:* Security Clearance: Active Top Secret clearance with eligibility for Sensitive Compartmented Information (SCI).
- Bachelor's degree in Systems Engineering, Software Engineering, Computer Science, Data Science, Aerospace Engineering, or a related technical discipline.
- Minimum of 20 years of experience in systems engineering, with a focus on ground systems architecture and standards.
- Experience developing or working with architectural reference models or frameworks is highly desired.
- Experience applying MBSE methodologies in DoD environments is preferred, especially in the context of architecture modeling.
- Proficiency in MBSE tools such as Cameo Systems Modeler, MagicDraw, or Enterprise Architect is highly desirable.
- Experience supporting ground systems, mission systems, command and control systems, defense systems, or other complex technical architectures.
- Strong understanding of systems engineering principles, architecture development, requirements analysis, interface definition, integration, verification, and technical decision\-making.
- Experience with DevSecOps, CI/CD pipelines, software delivery workflows, or modern software engineering environments.
- Working knowledge of AI/ML concepts, data analytics, large language models, agentic workflows, retrieval\-augmented generation, or applied automation.
- Ability to translate architecture and engineering problems into data\-driven or AI/ML\-enabled solution approaches.
- Ability to work across systems engineering, software, cybersecurity, cloud/platform, test, and program management teams.
Desired:* Familiarity with MLOps, model evaluation, prompt engineering, AI governance, AI assurance, or secure deployment of AI\-enabled capabilities.
- Experience with GitLab, Jenkins, Kubernetes, containers, cloud environments, artifact repositories, automated test frameworks, or pipeline observability tools.
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Perks and Benefits
======================
- *Vacation:*
New Hires Accrue 20 days of PTO and 10 Holidays per Year
- *Health Insurance:*
Zero Deductible Health Plans
- *Flexible Schedules:*
Flex Schedules
- *Professional Development:*
Up to $10,000 Annual Education/Training Reimbursement
- *ESOP:*
Funded Stock Ownership Plan
- *401k Match\+:*
6% 401k Match \+ Immediate Vesting
- *Bonus Program:*
Semi\-Annual Bonus Opportunity
- *Mentorship:*
Career Mentorship Programs
### Why is MTSI a great place to work
- *Interesting Work:*
Our co\-workers support some of the most important and critical programs to our national defense and security.
- *Values:*
Our first core value is that employees come first. We challenge our co\-workers to provide the highest level of support and service, and reward them with some of the best benefits in the industry.
- *100% Employee Owned:*
We have a stake in each other's success, and the success of our customers. It's also nice to know what's going on across the company; we have company wide town\-hall meetings three times a year.
- *Great Benefits \- Most Full\-Time Staff Are Eligible for:*
- + Starting PTO accrual of 20 days PTO/year \+ 10 holidays/year
+ Flexible schedules
+ 6% 401k match with immediate vesting up to $9k annually
+ Semi\-annual bonus eligibility (July and Decemeber)
+ Company funded Employee Stock Ownership Plan (ESOP) \- a separate qualified retirement account
+ Up to $10,000 in annual educational reimbursement
+ Other company funded benefits, like life and disability insurance
+ Optional zero deductible Blue Cross/Blue Shield health insurance plan
- *Track Record of Success:*
We have grown every year since our founding in 1993\.
Modern Technology Solutions, Inc. (MTSI) is a 100% employee\-owned engineering services and solutions company that provides high\-demand technical expertise in Digital Transformation, Modeling and Simulation, Rapid Capability Development, Test and Evaluation, Artificial Intelligence, Autonomy, Cybersecurity and Mission Assurance
MTSI delivers capabilities to solve problems of global importance. Founded in 1993, MTSI today has employees at over 20 offices and field sites worldwide.
For more information about MTSI, please visitwww.mtsi\-va.com
#### EEO Statement
MTSI embraces nine core values including our first core value of Employees come first. Consistent with our Core Values, we are committed to Equal Opportunity, making decisions without regard to race, color, religion, sex, national origin, age, military/veteran status, disability, or any other characteristics protected by applicable law. MTSI is committed to Equal Employment Opportunity and providing reasonable accommodations to applicants and employees with physical and/or mental disabilities.
### Why is MTSI a great place to work
- Interesting Work: Our co\-workers support some of the most important and critical programs to our national defense and security.
- Values: Our first core value is that employees come first. We challenge our co\-workers to provide the highest level of support and service, and reward them with some of the best benefits in the industry.
- 100% Employee Owned: We have a stake in each other's success, and the success of our customers. It's also nice to know what's going on across the company; we have company wide town\-hall meetings three times a year.
- Great Benefits \- Most Full\-Time Staff Are Eligible for:
- + Starting PTO accrual of 20 days PTO/year \+ 10 holidays/year
+ Flexible schedules
+ 6% 401k match with immediate vesting up to $9k annually
+ Semi\-annual bonus eligibility (July and Decemeber)
+ Company funded Employee Stock Ownership Plan (ESOP) \- a separate qualified retirement account
+ Up to $10,000 in annual educational reimbursement
+ Other company funded benefits, like life and disability insurance
+ Optional zero deductible Blue Cross/Blue Shield health insurance plan
- Track Record of Success: We have grown every year since our founding in 1993\.
Modern Technology Solutions, Inc. (MTSI) is a 100% employee\-owned engineering services and solutions company that provides high\-demand technical expertise in Digital Transformation, Modeling and Simulation, Rapid Capability Development, Test and Evaluation, Artificial Intelligence, Autonomy, Cybersecurity and Mission Assurance
MTSI delivers capabilities to solve problems of global importance. Founded in 1993, MTSI today has employees at over 20 offices and field sites worldwide.
For more information about MTSI, please visit www.mtsi\-va.com
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 MTSI, 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. Mid-level AI roles across all categories have a median of $194,400.
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.
MTSI AI Hiring
MTSI has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dayton, OH, US.
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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