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-
objective 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).
Domain Application & LPR: Engineer end-to-end computer vision workflows,
including License 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 pipelines.
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 |
Edge & Cloud Inference
#J-*****-Ljbffr

📌 Computer Vision / Ai Systems Engineer (Córdoba)
🏢 Wakapi
📍 Córdoba