Ai Computer Vision Engineer - Remote - Latin America

Fullstack Labs, Inc.; Ciudad de Mendoza; Remote; ARS 1,000,000 - 1,600,000; Full time

Job summary

Fullstack Labs, Inc. is seeking an experienced computer vision/ML engineer to build production AI vision systems. You will design, train, and deploy models for object detection, attribute extraction, and OCR from real-time image and video streams.

You will implement low‑latency inference across cloud and edge devices, and architect robust pipelines for high‑throughput processing, including LPR/OCR and multi‑object tracking capabilities.

Qualifications

• 3+ years of hands-on experience designing and deploying CV and DL models in production.
• Deep expertise with modern object detection architectures (YOLO, DETR, Faster R‑CNN, ViTs).
• Strong Python skills and experience with PyTorch, OpenCV, and TensorFlow.
• Experience with video stream ingestion, frame processing, and low-latency inference.
• Experience with LPR/OCR and attribute inference systems.

Responsibilities

• Design, train, and fine‑tune state‑of‑the‑art vision models for production.
• Architect, develop, and maintain streaming pipelines for high‑throughput video/text data.
• Implement low‑latency inference on cloud and edge devices using TensorRT/ONNX.
• Curate datasets, annotations, and automated retraining/evaluation loops.

Skills

Vision model deployment

YOLO/DETR/ViT

Python programming

PyTorch/OpenCV/TF

Video stream processing

Low-latency inference

LPR/OCR

Model quantization/ONNX/TensorRT

Tools

PyTorch

OpenCV

TensorFlow

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 |