Panoptyc - Lead Hardware Engineer

• 5+ years of hands-on experience with embedded Linux systems and edge hardware deployment in production environments • Deep expertise with AWS IoT Core and AWS Greengrass — device provisioning, fleet management, component deployment pipelines, and OTA updates • Strong Python programming skills with experience writing production-quality services and tooling (not just scripts) • Fluency with Linux systemd — writing unit files, managing dependencies, watchdogs, journald integration, and failure recovery • Experience with the Yocto Project for building custom embedded Linux distributions tailored to specific hardware targets and minimal production footprints • Solid Docker experience including multi-stage builds, resource constraints, container networking, and orchestrating multiple services on resource-constrained hardware • Hands-on experience with RTSP-based camera integration and ONVIF protocol for camera discovery and management • Experience integrating with POS or other retail transaction systems at the data or protocol level • Practical experience with NVIDIA Jetson devices (Nano, Orin NX, AGX, or equivalent) and running AI inference workloads on them • Hands-on experience with Raspberry Pi Compute Module platforms (CM4 and/or CM5) in production hardware design or deployment • Proven ability to design for failure: reconnection logic, graceful degradation, remote observability, and recovery automation • Familiarity with SOC 2 environments — change management, access controls, and auditability for device fleets • Exposure to computer vision pipelines and ML model deployment beyond the hardware/runtime layer • Familiarity with hardware-aware model optimization — TensorRT, ONNX, quantization, and CPU/memory affinity configuration • Experience with retail technology ecosystems — loss prevention, CCTV, or transaction audit systems • Background in custom PCB design, carrier board selection, or hardware BOM ownership • Beyond the technical checklist, we care about how you work. You're the kind of engineer who reads error logs before asking questions. You hold a deployment in your head end-to-end — from the Python process on the device to the Greengrass component to the IoT shadow in AWS — and you notice when something doesn't add up. You don't romanticize complexity; you reduce it. And when something breaks in the field at an inconvenient time, your instinct is to get to root cause, not just restore service. • We're a small, high-output team. Autonomy is real here, and so is accountability.