Computer Vision / AI Systems Engineer

Wakapi; Ciudad de Mendoza; On-site; ARS 1,200,000 - 2,400,000; Full time

Job summary

Wakapi is seeking an experienced computer vision engineer to design and deploy production-ready vision models for real-time attribute extraction from image and video streams. You will build low-latency pipelines, optimize inference on cloud and edge devices, and extend CV workflows including LPR/OCR and multi-object tracking.

The role emphasizes strong Python and PyTorch/OpenCV skills, model quantization, and ONNX/TensorRT deployment to accelerate performance in diverse environments.

Qualifications

• 3+ years of hands-on experience designing and deploying computer vision models in production.
• 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 with video stream ingestion and low-latency inference pipelines.
• Experience with License Plate Recognition (LPR)/OCR or fine-grained attribute inference systems.
• Familiarity with model quantization, ONNX export, and TensorRT compilation.

Responsibilities

• Model Architecture & Development: Design, train, and fine-tune state-of-the-art vision models for object detection and attribute extraction.
• Stream Processing & Pipeline Engineering: Build low-latency ingestion and processing pipelines for high-throughput video frames.
• Domain Application & LPR: Engineer CV workflows including LPR/OCR and multi-object tracking.
• Inference Optimization: Optimize performance on cloud (Kubernetes/GCP) and edge devices with TensorRT, ONNX, or OpenVINO.
• Dataset & Active Learning: Curate, annotate, and augment visual datasets with automated retraining loops.

Skills

Python programming

Deep learning

Video processing

Object detection

Tools

PyTorch

OpenCV

TensorFlow

TensorRT

ONNX

OpenVINO

Kubernetes

GCP

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).
• 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

C - PGS - Wakapi Web