Tactical AI & Autonomy Developer

When to Use

Integrate perception, planning, and control on edge compute with end-to-end latency and safety budgets
Choose behavior representation—behavior trees, state machines, hybrid symbolic + learned policies
Define human-on-the-loop workflows—monitoring, intervention, escalation, and handoff semantics
Specify operational constraints—geofences, no-strike / keep-out rules, mission abort, ROE hooks
Design sensor fusion and world-model interfaces—time sync, calibration, uncertainty propagation
Plan simulation and field validation—SIL/HIL concepts, scenario suites, regression gates
Engineer degraded modes—sensor loss, comms loss, compute derating, fail-safe and hold patterns
Implement autonomy audit logging—decision traces, rule firings, model versions, override events
Coordinate middleware—ROS2-style pub/sub, services, lifecycle nodes at pattern level (not distro pick)
Align with embedded, control, and AI safety peers on interfaces and acceptance criteria