ML Ops Engineer - Clearance Required

OverviewLMI is seeking an ML Ops Engineer to support the operationalization, sustainment, and continuous improvement of computer vision models used on autonomous edge platforms for a Special Operations customer.

This role is responsible for the lifecycle management of machine learning models that operate onboard disconnected edge systems in tactical environments. A successful ML Ops Engineer ensures models remain accurate, testable, versioned, and safely deployable without requiring operators to be AI experts.

This position bridges field operations, data science, and autonomy software to ensure models improve over time without degrading mission performance or introducing unsafe behavior.

This position requires an active Secret clearance. LMI is a new breed of digital solutions provider dedicated to accelerating government impact with innovation and speed.

Investing in technology and prototypes ahead of need, LMI brings commercial-grade platforms and mission-ready AI to federal agencies at commercial speed. Leveraging

our mission

-ready technology and solutions, proven expertise in federal deployment, and strategic relationships, we enhance outcomes for the government, efficiently and effectively. With a focus on agility and collaboration, LMI serves the defense, space, healthcare, and energy sectors—helping agencies navigate complexity and outpace change. Headquartered in Tysons, Virginia, LMI is committed to delivering impactful results that strengthen missions and drive lasting value. ResponsibilitiesSolution Design:Design the ML lifecycle for computer vision models operating on edge platforms Establish model versioning, validation, and deployment patterns suitable for disconnected tactical environments Develop guardrails to ensure autonomy behavior remains predictable and auditable Create architectures for collecting operational data and feeding it back into retraining pipelines Development:Build and maintain pipelines for model packaging, testing, and deployment to edge systems Implement automated testing to ensure new models do not degrade performance Develop repeatable processes so operators can update systems without ML expertise Integrate data science outputs into fieldable, supportable software packages Testing and Quality Assurance:Validate model performance against real operational data Conduct regression testing to ensure updated models maintain or improve detection and tracking performance Ensure traceability of which model versions were used during specific operations Maintenance and Support:Support field units in updating and maintaining onboard models Troubleshoot issues related to model performance and deployment in operational environments Continuously improve processes for safe model iteration and deployment Documentation:Create technical documentation for model lifecycle processes Develop operator friendly guides for updating and validating onboard systems Document model versioning, testing results, and deployment procedures

Qualifications

:Experience implementing ML Ops practices for computer vision or edge autonomous systems Understanding of model versioning, validation, and deployment pipelines Experience working with disconnected or bandwidth constrained environments Familiarity with containerization and packaging of ML models for deployment Understanding of how to translate data science outputs into operational software Strong problem solving and analytical skills Ability to work independently and as part of a team Excellent communication and interpersonal skills Must possess an active Secret clearance Preferred

Qualifications

:Experience with autonomous systems, robotics, or unmanned platforms Experience supporting Special Operations or tactical technology programs Familiarity with computer vision model development and evaluation Experience designing data pipelines for model retraining from field collected data Understanding of responsible AI principles and human in the loop autonomy systems The target

salary range

for this position is $140,000 - 185,

000.

The

salary range

displayed represents the typical

salary range

for this position and is not a guarantee of compensation.

Individual salaries are determined by various factors including , but not limited to

location

, internal equity, business considerations, client contract

requirements

, and candidate

qualifications

, such as education, experience, skills, and security clearances.