Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
AI/ML Systems Engineer
Dayton, OH, US
Live, first seen by us 83 days ago- Pay
- Pay not listedSimilar roles state $166K–$240K
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s posting: FULL_TIME
From the employer’s posting
Before you apply
- Work authorization
- Citizens only
Security Clearance: Active Top Secret clearance with eligibility for Sensitive Compartmented Information (SCI)
What you’ll work on
Full postingLead development and documentation of the Government Reference Architecture for ground segments
Use agentic AI to support trade studies, risk identification, and modernization planning
Develop AI-enabled analysis of large engineering datasets like requirements, interface data, and test results
Improve CI/CD and DevSecOps pipelines with AI/ML-assisted automation and anomaly detection
Write GRA technical documentation, including architecture diagrams and interface specifications
From the employer’s posting
Lead the development and documentation of the Government Reference Architecture (GRA) for ground segments
Use agentic AI methods to support architecture trade studies, design decision analysis, risk identification, technical baseline assessment, modernization planning, and mission/thread analysis
Develop AI-enabled approaches for analyzing large engineering datasets, including requirements, architecture artifacts, interface data, test results, operational data, defect trends, and technical documentation
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
Develop and deliver comprehensive technical documentation for the GRA, including architectural diagrams, interface specifications, and implementation guidelines
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Aug 13, 202683 days ago
- Recorded sightings
- 369
- Last seen by us
- Nov 4, 2026
- Employer says posted
- Jul 27, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 83
Up longer than most. Similar jobs usually come down between day 6 and day 69.
Well past Modern Technology Solutions’s usual: its jobs come down by about day 29.
From 4,457 similar postings that came down, and 47 of Modern Technology Solutions’s. Coming down is not always a hire.
Modern Technology Solutions is hiring
13 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Hiring has slowed
- Takes jobs down at a usual pace
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Kubernetes
Where the posting names them
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. Develop and deliver comprehensive technical documentation for the GRA, including architectural diagrams, interface specifications, and implementation guidelines. Evaluate emerging AI/ML, agentic AI, data engineering, and Machine Learning Operations (MLOps) technologies for applicability to ground systems and digital engineering environments. 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. Experience with GitLab, Jenkins, Kubernetes, containers, cloud environments, artifact repositories, automated test frameworks, or pipeline observability tools.
Your next step
Complete your application on talent.mtsi-va.com. The employer’s form will show what is required.
Applying through Eightfold
- You sign in, with Microsoft, LinkedIn, Google, Facebook or a new account, before the application opens.
Already applied? Track this application
Demand for ML Engineer roles
New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
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. Develop and deliver comprehensive technical documentation for the GRA, including architectural diagrams, interface specifications, and implementation guidelines. Evaluate emerging AI/ML, agentic AI, data engineering, and Machine Learning Operations (MLOps) technologies for applicability to ground systems and digital engineering environments. 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. Experience with GitLab, Jenkins, Kubernetes, containers, cloud environments, artifact repositories, automated test frameworks, or pipeline observability tools.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Dayton, OH, US
Working pattern and location restrictions need checking in the full posting.
- Work authorization
This passage needs a closer read in the full description.