Dev Ops Enginner/System Engineer

Cognizant; Ciudad de Mendoza; On-site; ARS 1,800,000 - 3,200,000; Full time

Job summary

Cognizant in Mendoza, Argentina seeks a hands-on AI Computer Vision Engineer to design and deploy production-grade vision models for real-time attribute extraction from video streams.

You will build low-latency pipelines, optimize inference on cloud (Kubernetes/GCP) and edge devices, work with LPR/OCR and state-of-the-art detectors, and contribute to dataset curation and active learning loops with PyTorch and OpenCV. 3+ years of practical CV experience required.

Qualifications

• 3+ years of hands-on experience designing and deploying computer vision models in production.
• Deep expertise with modern object detectors (YOLO, DETR, Faster R-CNN, ViT).
• Strong programming in Python and DL frameworks (PyTorch, OpenCV, TensorFlow).
• Proven experience with video stream ingestion, frame processing and low-latency inference.
• Experience with LPR, OCR, or attribute inference systems.
• Familiarity with quantization, ONNX export, and TensorRT compilation.

Responsibilities

• Model Architecture & Development: design, train, and fine-tune vision models for object detection and attribute extraction.
• Stream Processing & Pipeline Engineering: build low-latency ingestion and processing pipelines for video streams.
• Plate Recognition, OCR, and multi-object tracking.
• Inference Optimization: optimize performance on cloud (Kubernetes/GCP) and edge devices using TensorRT/ONNX/OpenVINO.
• Dataset & Active Learning: curate and augment data, setup automated retraining loops and evaluation.

Skills

Python

Deep learning

YOLO/DETR

Video processing

LPR/OCR

TensorRT

ONNX export

Tools

PyTorch

OpenCV

TensorFlow

ONNX

TensorRT

Job description

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 |