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
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
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
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
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

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

ML Ops Engineer

Key skills