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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Embedded Edge AI Architect - **Company:** Trebecon LLC - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, C++ (Programming Language), Nvidia CUDA, Information Engineering, Data Integrity, Linux on Embedded Systems, Embedded Software, Firmware, Field-Programmable Gate Array (FPGA), FreeRTOS, Python (Programming Language), Machine Learning, Multipoint Control Unit, OpenGL, OpenCL, Performance Tuning, Real-Time Operating Systems, Tensorflow, DataOps, Signal Processing, Data Processing, Graphics Processing Unit (GPU), Cloud Platform System, High Performance Computing, PIC Microcontroller, Data Ingestion, Pytorch, Delivery Pipeline, ONNX (Open Neural Network Exchange) Format, Real Time Data, Data Management, TensorRT, Data Pipelines - **Published:** June 30, 2026 - **Apply:** https://www.dice.com/job-detail/434d3db4-d7d9-4804-8f4b-bdd785d0a5db ## About the Role * 8-10+ years of experience in Embedded Systems, Edge AI, AI/ML, Data Engineering, Telemetry, or Applied Data Science. * Strong proficiency in Python for data processing, AI/ML modeling, and pipeline development. * Hands-on experience with C/C++ for embedded software and firmware integration. * Experience developing and deploying Edge AI applications on embedded devices. * Strong knowledge of AI/ML frameworks such as TensorFlow, PyTorch, TensorFlow Lite, Core ML, TensorRT, or ONNX Runtime. * Experience working with embedded platforms including ARM processors, NVIDIA GPUs, DSPs, FPGAs, or Microcontrollers (MCUs). * Proven experience designing and building telemetry pipelines, data ingestion frameworks, and data models. * Experience translating hardware and firmware telemetry into structured datasets for analytics, performance tuning, and machine learning. * Strong understanding of time-series data, signal processing, and real-time data systems. * Experience developing firmware-to-application interfaces and telemetry ingestion pipelines. * Ability to work across multiple system layers, including embedded devices, transport, host systems, and data platforms. * Familiarity with GPU-accelerated computing and high-performance computing environments. * Experience collaborating with firmware, hardware, and platform engineering teams. * Experience designing performance optimization models based on telemetry, workload behavior, thermal constraints, and system utilization. Preferred Qualifications * Experience deploying AI inference pipelines on NVIDIA Jetson, CUDA, or TensorRT. * Experience building Edge AI applications using Core ML, Metal, TensorFlow Lite, or OpenCL/OpenGL ES. * Knowledge of Embedded Linux environments. * Familiarity with RTOS concepts such as Zephyr or FreeRTOS. * Experience with platform management protocols such as MCTP and PLDM. * Exposure to hybrid Edge/Cloud architectures. * Experience with data observability, telemetry validation, and data integrity frameworks. * Background in hardware-centric environments such as IoT, Robotics, Autonomous Systems, Consumer Devices, or Industrial Automation. Key Technologies * Programming: Python, C/C++ * AI/ML: TensorFlow, PyTorch, TensorFlow Lite, Core ML, TensorRT, ONNX Runtime * Embedded Platforms: ARM, NVIDIA GPU, DSP, FPGA, MCU * Operating Systems: Embedded Linux, RTOS (Zephyr, FreeRTOS) * GPU Technologies: CUDA, TensorRT * Telemetry & Analytics: Time-Series Data, Signal Processing, Data Pipelines * Protocols: MCTP, PLDM ## Description We are seeking an experienced Embedded Edge AI Architect with strong expertise in AI/ML modeling, embedded systems, and telemetry-driven performance optimization. The ideal candidate will have hands-on experience developing and deploying AI solutions on embedded platforms such as ARM processors, GPUs, DSPs, FPGAs, and MCUs, with a strong background in Python, C/C++, and Edge AI frameworks. ## Related Videos - [Trends, Challenges and Best Practices for AI at the Edge](https://www.wearedevelopers.com/videos/630-trends-challenges-and-best-practices-for-ai-at-the-edge) - [Edge Orchestration for the Physical World: Connecting Cameras, Sensors, and Devices with MQTT](https://www.wearedevelopers.com/videos/100081-edge-orchestration-for-the-physical-world-connecting-cameras-sensors-and-devices-with-mqtt) - [RTX AI PC: Developing local and edge AI applications](https://www.wearedevelopers.com/videos/100078-rtx-ai-pc-developing-local-and-edge-ai-applications) ## Related Articles - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Best US AI Conferences for CTOs in 2026: Build vs. Buy, Vendor Evaluation, and Peer Intelligence](https://www.wearedevelopers.com/magazine/736-best-us-ai-conferences-for-ctos-in-2026-build-vs-buy-vendor-evaluation-and-peer-intelligence) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Dev Digest 132 - Binging WADFlix?](https://www.wearedevelopers.com/magazine/473-dev-digest-132-binging-wadflix)