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 (PyTorch OpenCV TensorFlow).

• 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 PyTorch / OpenCV Video Stream Processing LPR / OCR

Edge & Cloud Inference

C - PGS -

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