Egocentric Video Data Contributor

Perle Systems; Ciudad de Mendoza; In loco; ARS 1.800.000 - 3.000.000; Tempo pieno

Descrizione del lavoro

Perle Systems in Mendoza is seeking an experienced Computer Vision Engineer to design and deploy production AI vision systems for real-time attribute extraction from image and video streams. You will work on state-of-the-art models and build low-latency processing pipelines to support high-throughput video streams.

You will train and fine-tune detectors such as YOLO, DETR, and ViT, implement LPR/OCR and MOT, and optimize performance on cloud and edge devices using TensorRT, ONNX, and OpenVINO.

Competenze

• 3+ years of hands-on experience in production CV and deep learning systems.
• Deep expertise with modern object detection architectures (YOLO, DETR, Faster R-CNN, ViT).
• Strong programming in Python and DL frameworks (PyTorch, OpenCV, TensorFlow).
• Experience with video stream ingestion, frame processing, and low-latency inference.
• Experience with LPR, OCR, or fine-grained attribute inference systems.
• Familiarity with model quantization, ONNX export, and TensorRT compilation.

Mansioni

• Research and architect production AI vision systems for real-time attribute extraction from image/video streams.
• Train and fine-tune state-of-the-art vision models (YOLO, DETR, transformers) for object detection and attribute extraction.
• Build low-latency pipelines for ingestion and processing of high-throughput video and image streams.
• Develop plate recognition (LPR/ANPR), OCR, and multi-object tracking capabilities.
• Optimize model performance on cloud (Kubernetes/GCP) and edge devices using TensorRT, ONNX, OpenVINO.
• Curate and augment datasets; establish automated retraining loops and evaluation frameworks.

Conoscenze

Python programming

PyTorch

OpenCV

TensorFlow

Video processing

Low-latency inference

Strumenti

YOLO

DETR

Faster R-CNN

Vision Transformers

TensorRT

ONNX

OpenVINO

GCP

Kubernetes

Descrizione del lavoro

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.

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).

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

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 |