Panoptyc
1 - 10 employees
🤖 Artificial Intelligence
🔐 Security
🔧 Hardware
Artificial Intelligence • Security • Hardware
Panoptyc is a company dedicated to combating theft in micro markets using advanced surveillance technology. They offer a combined software and hardware solution that leverages artificial intelligence to recognize suspicious behavior and alert operators, significantly reducing shrinkage amounts and saving operators time and money. With state-of-the-art cameras and intelligent software that automatically highlights suspicious incidents, Panoptyc empowers teams to efficiently address and manage theft without the hassle of reviewing endless footage. Their system makes footage accessible even when faced with network issues, offering a powerful solution for micro market theft detection.
Lead Hardware Engineer
🕒 April 30
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Panoptyc
1 - 10 employees
🤖 Artificial Intelligence
🔐 Security
🔧 Hardware
Artificial Intelligence • Security • Hardware
Panoptyc is a company dedicated to combating theft in micro markets using advanced surveillance technology. They offer a combined software and hardware solution that leverages artificial intelligence to recognize suspicious behavior and alert operators, significantly reducing shrinkage amounts and saving operators time and money. With state-of-the-art cameras and intelligent software that automatically highlights suspicious incidents, Panoptyc empowers teams to efficiently address and manage theft without the hassle of reviewing endless footage. Their system makes footage accessible even when faced with network issues, offering a powerful solution for micro market theft detection.
📋 Description
• Design, configure, and maintain edge compute solutions on Raspberry Pi CM4/CM5, NVIDIA Jetson, and similar embedded Linux platforms • Own hardware selection and validation for new deployments, balancing compute headroom, thermal constraints, cost, and supply chain reliability • Architect and maintain systemd service definitions for reliable, observable, auto-recovering edge processes • Build and manage Docker container orchestration strategies for running CV inference workloads at the edge with efficient resource utilization • Own our AWS IoT Core integration — device provisioning, certificate management, shadow state, telemetry pipelines, and fleet-wide configuration • Design and maintain AWS Greengrass component deployments for managing edge workloads at scale across distributed device fleets • Build robust OTA update and rollback mechanisms that account for unreliable field connectivity • Integrate with IP camera ecosystems using RTSP stream ingestion and ONVIF device management and discovery protocols • Build and maintain integrations with POS systems to correlate transaction data with vision events in real time • Ensure video pipeline reliability including reconnection logic, frame integrity checks, and latency-aware buffering • Tune model inference for constrained hardware — quantization, TensorRT optimization on Jetson, ONNX runtime configuration, and CPU/GPU affinity settings • Profile and optimize memory, thermal, and power envelopes to sustain CV workloads on edge hardware with acceptable duty cycles • Evaluate new edge AI hardware as the landscape evolves and make informed recommendations on adoption • Actively leverage AI coding tools and LLM-assisted workflows as a force multiplier — this is an expectation, not a differentiator • Document architecture, deployment runbooks, and failure modes rigorously — the team that picks up a 2am alert needs to be set up to succeed • Collaborate across engineering, product, and installation/support teams; this role has significant cross-functional surface area
🎯 Requirements
• 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
🏖️ Benefits
• Health insurance • Professional development
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