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 - 29092026

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