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Tech Stack
Tools & technologiesAWSDockerIoTLinuxPython
About the role
Key responsibilities & impact- 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
What you’ll need- 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
Comp & perks- Health insurance
- Professional development
ATS Keywords
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Hard Skills & Tools
embedded LinuxAWS IoT CoreAWS GreengrassPythonLinux systemdYocto ProjectDockerRTSPONVIFNVIDIA Jetson
Soft Skills
collaborationdocumentationproblem-solvingcross-functional teamworkdesign for failure