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).

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 |

📌 Dev Ops Enginner/System Engineer (Mendoza)
🏢 Cognizant
📍 Mendoza