Computer Vision Engineer
4 days ago
Go Arrow Tech is excited to be supporting a leading organisation investing heavily in AI, automation, and intelligent perception systems. Our client is seeking a highly skilled Computer Vision Engineer to design and deploy advanced visual intelligence solutions that drive real-world automation, robotics, and analytics.
This is an excellent opportunity for engineers passionate about applying deep learning, image processing, and perception algorithms to solve complex problems at scale.
ole Overview
The Computer Vision Engineer will be responsible for developing, optimising, and deploying computer vision models and pipelines. You will work across image/video processing, deep learning, object detection, segmentation, tracking, OCR, and visual perception systems.
You will collaborate with data scientists, ML engineers, software developers, and robotics teams to create intelligent visual systems that perform reliably in production environments.
Key Responsibilities
As a Computer Vision Engineer, you will:
- Develop and optimise computer vision models for detection, classification, segmentation, pose estimation, or tracking.
- Work with deep learning architectures such as CNNs, transformers, YOLO, Mask R-CNN, UNet, and Vision Transformers (ViT).
- Build and maintain end-to-end vision pipelines, from data collection to deployment.
- Work with image, video, LiDAR, 3D, depth, or multi-sensor datasets.
- Implement and productionise vision models using TensorFlow, PyTorch, OpenCV, ONNX, or TensorRT.
- Collaborate on real-time perception for robotics, automation, security systems, and analytics.
- Optimise models for edge devices, GPUs, or real-time systems.
- Conduct experiments, evaluate performance, and refine visual algorithms.
- Document designs, findings, and improvements for cross-functional teams.
- Contribute to the overall AI and perception strategy of the organisation.
Job Types: Full-time, Permanent, Fixed term contract
Contract length: 12 months
Pay: £50,880.43-£111,967.02 per year
Work Location: In person
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