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
When NOT to Use
- General LLM/RAG products, chat agents, or cloud inference features →
- LLM jailbreak / app red team engagements and ROE →
- Safeguard gateway serving, GPU routing, moderation infra SLOs →
ml-infrastructure-engineer-safeguards
- AI governance, risk tiers, model cards, compliance mapping only →
- Bare-metal MCU firmware, ISR/RTOS, drivers without autonomy stack →
embedded-real-time-software-engineer
- Plant PLC/DCS, historian, OT scan cycles, Modbus/DNP3 plant logic →
control-software-developer
- HIL security bench, bus fault injection, authorized exploitation on rigs →
hardware-in-the-loop-security-tester
- Adversarial ML robustness (evasion/poison on models in lab) →
ai-adversarial-robustness-engineer
- Export-controlled weapon design detail or customer-specific classified architectures → legal / program office; keep outputs generic
Related skills
| Need | Skill |
|---|
| Production LLM/RAG and agent features | |
| LLM red team and jailbreak policy | |
| Safeguard serving and inference platform | ml-infrastructure-engineer-safeguards
|
| Governance, risk tiers, model cards | |
| MCU/RTOS, drivers, WCET on chip | embedded-real-time-software-engineer
|
| PLC/DCS, OT protocols, plant control apps | control-software-developer
|
| HIL security assessment on benches | hardware-in-the-loop-security-tester
|
| Adversarial robustness on ML models | ai-adversarial-robustness-engineer
|
Core Workflows
1. Scope and platform constraints
Capture mission class, latency chain, safety intent, compute envelope, and test environments before stack design.
See references/tactical_ai_autonomy_scope.md
2. Perception–planning–control stack
Partition pipelines, interfaces, timing, and responsibility between learned and symbolic components.
See references/perception_planning_control_stack.md
3. Safety, rules, and human oversight
Define geofencing, constraint rules, HITL escalation, and abort semantics with traceable enforcement points.
See references/safety_human_oversight_and_rules.md
4. Simulation and validation
Build scenario matrices, sim-to-real gaps, metrics, and release gates from SIL through limited field trials.
See references/simulation_testing_and_validation.md
5. Degraded modes and fail-safe
Specify detection, transitions, and safe outcomes for sensor, comms, and compute failures.
See references/degraded_modes_and_fail_safe.md
6. Deployment, logging, and audit
Plan edge deployment, OTA boundaries, structured autonomy logs, and post-incident reconstruction.
See references/deployment_logging_and_audit.md
Outputs
- Autonomy architecture brief — PPC boundaries, rates, compute map, middleware topology
- Behavior spec — states/modes, BT or policy outline, preconditions and timeouts
- Safety rules pack — geofences, constraints, abort triggers, enforcement layer mapping
- HITL playbook — roles, UI cues, override logging, escalation paths
- Validation plan — scenarios, metrics, pass/fail gates, sim vs field phases
- Degraded-mode matrix — triggers, transitions, safe states, recovery rules
- Audit schema — fields per decision cycle, retention, correlation IDs
Principles
- Safety before capability — prove constraint enforcement and abort paths before expanding autonomy
- Traceable decisions — every safety-critical branch logs rule ID, inputs hash, and outcome
- Deterministic fallbacks — symbolic safe modes when learned components are uncertain or unavailable
- Measured latency — budget per stage; no stack design without end-to-end timing evidence
- Sim ≠ field — document sim assumptions; require field scenarios for release-critical behaviors
- Generic documentation — UAS/autonomous systems terms only; no named customers or controlled technical dumps