Impactful Ai Systems Architect (Python) | Remote & Flexible

BairesDev; Ciudad de Mendoza; Híbrido; ARS 1.800.000 - 3.000.000; Jornada completa

Descripción de la vacante

BairesDev is seeking a seasoned Computer Vision Engineer to design and deploy production-grade vision systems capable of real-time inference on video streams. You will architect, train, and fine-tune state-of-the-art models (YOLO, DETR, Transformers) for object detection, classification, and attribute extraction.

You will build low-latency pipelines for high-throughput streams, implement LPR/OCR components, and optimize deployments on cloud (Kubernetes/GCP) and edge devices using TensorRT, ONNX,

Formación

• 3+ years of hands-on experience designing and deploying computer vision models in production.

Responsabilidades

• Research, architectures, and construct production AI vision systems in real time.
• Design, train, and fine-tune YOLO/DETR/ViT models for detection and attribute extraction.
• Build low-latency ingestion and processing pipelines for video/image streams (RTSP/WebRTC).
• Perform LPR/OCR and multi-object tracking among streams.
• Optimize inference for cloud (Kubernetes/GCP) and edge devices using TensorRT/ONNX/OpenVINO.
• Curate datasets, annotate, and establish automated retraining and evaluation loops.

Conocimientos

YOLO

DETR

Computer Vision

PyTorch

OpenCV

Video Stream Processing

LPR

OCR

Herramientas

PyTorch

OpenCV

TensorFlow

ONNX

TensorRT

OpenVINO

Descripción del empleo

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 |