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