Computer Vision / AI Systems Engineer

The Role: Researches, architectures, and constructs production AI vision systems, leveraging state-of-the-art detection models and stream processing pipelines to extract attributes and infer intelligence from image and video streams in real time.

Responsibilities

Model Architecture & Development: Design, train, and fine-tune state-of-the-art vision models (e.g., YOLO variants, DETR, Transformers) for object detection, classification, and attribute extraction. Stream Processing & Pipeline Engineering: Build low-latency ingestion and processing pipelines for high-throughput video and image streams (e.

g., RTSP, WebRTC, frame decoding). Domain Application & LPR: Engineer end-to-end computer vision workflows, including License Plate Recognition (LPR/ANPR), Optical Character Recognition (OCR), and multi-object tracking (MOT). Inference Optimization: Optimize model performance for efficient execution on cloud infrastructure (Kubernetes/GCP) and edge devices using TensorRT, ONNX, or OpenVINO.

Dataset & Active Learning: Curate, annotate, and augment visual datasets, establishing automated model retraining loops and evaluation frameworks.

Requirements

3+ years of hands-on experience designing and deploying computer vision and deep learning models in production environments. Deep expertise with modern object detection architectures (YOLO, DETR, Faster R-CNN, Vision Transformers). Strong programming skills in Python and deep learning frameworks (Py. Torch, OpenCV, Tensor.

Flow). Proven experience working with video stream ingestion, frame processing, and low-latency inference pipelines. Demonstrated experience with License Plate Recognition (LPR), OCR, or fine-grained attribute inference systems. Familiarity with model quantization, ONNX export, and TensorRT compilation for hardware acceleration.

YOLO / DETR | Computer Vision | Py. Torch / OpenCV | Video Stream Processing | LPR / OCR | Edge & Cloud Inference C

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