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# Sr. Embedded/C++ Computer Vision Engineer - Hybrid

- **Company:** Cognizant Technology Solutions Corporation
- **Location:** Irving, TX, United States
- **Experience:** Expert
- **Salary:** $90,000.0 - $125,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Computer Vision, C++ (Programming Language), Program Optimization, Linux, Memory Management, Linux on Embedded Systems, Embedded Software, Networking Hardware, Python (Programming Language), Machine Learning, Object Detection, OpenCV, Performance Tuning, Tensorflow, Video Editing, Openwrt, RTSP, Containerization, ONNX (Open Neural Network Exchange) Format, Machine Learning Operations, Video Streaming, Lxc, Docker
- **Published:** September 19, 2026
- **Apply:** https://dejobs.org/x/x/3C439DB4A1354013A387A5330EA7BEBB/job/

## About the Role

Role Overview Looking for an engineer to optimize and deploy video analytics applications on embedded router hardware. The ideal candidate should have strong C++ development skills, experience with computer vision/video analytics, and the ability to work with Python-based AI/ML pipelines., * 4+ years of experience in Embedded Software Engineering.

 * Strong C++ programming skills.
 * Good understanding of Python and AI/ML workflows.
 * Experience with Video Analytics, Computer Vision, OpenCV, YOLO, or Object Detection.
 * Embedded Linux development and performance optimization.
 * Docker or containerization experience.
 * Knowledge of ONNX, TensorFlow Lite, quantization, or model optimization.

Preferred

 * Edge AI / Edge ML deployment experience.
 * OpenWRT, PrplOS, Cradlepoint, or networking device experience.
 * RTSP/video streaming knowledge.
 * ARM platform optimization.

## Description

* Port and optimize video analytics applications for CPU-based embedded devices.
 * Improve performance, memory usage, and inference speed on Linux platforms.
 * Work with object detection and image/video processing pipelines.
 * Deploy and manage applications in Docker/LXC containers.
 * Integrate and optimize AI/ML models using ONNX, TensorFlow Lite, or OpenVINO.
 * Benchmark and tune accuracy vs. performance on constrained hardware.


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