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 |

📌 Ai Computer Vision Engineer (Mendoza)
🏢 FullStack Labs
📍 Mendoza