• 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