Computer Vision / AI Systems Engineer

Wakapi; Departamento Capital; Presencial; ARS 1.200.000 - 2.400.000; Jornada completa

Descripción de la vacante

Wakapi seeks a seasoned computer vision engineer to design and deploy production‑level AI vision systems. You will develop state‑of‑the‑art detection models and real‑time pipelines for high‑throughput video and image streams, including LPR/OCR workflows.

You will optimize performance on cloud and edge devices, curate datasets, and establish automated retraining loops. Strong Python/DL framework skills and experience with modern architectures are essential.

Formación

• 3+ years of hands‑on CV and deep learning model deployment in production.
• Expertise with YOLO, DETR, Faster R‑CNN, Vision Transformers.
• Proficient Python and DL frameworks: PyTorch, OpenCV, TensorFlow.
• Experience with video stream ingestion and low‑latency inference.

Responsabilidades

• Model Architecture & Development: Design, train, and fine‑tune state‑of‑the‑art object detection models.
• Stream Processing & Pipeline Engineering: Build low‑latency ingestion and processing pipelines for high‑throughput video/image streams.
• Domain Application & LPR: Engineer end‑to‑end CV workflows including LPR/OCR and multi‑object tracking.
• Inference Optimization: Optimize execution on cloud (Kubernetes/GCP) and edge devices using TensorRT/ONNX/OpenVINO.
• Dataset & Active Learning: Curate, annotate, and augment datasets; automate retraining loops and evaluation.

Conocimientos

Computer Vision

Deep Learning

YOLO

DETR

PyTorch

OpenCV

TensorFlow

Python

Video Streams

LPR OCR

TensorRT

ONNX

OpenVINO

Kubernetes

GCP

Herramientas

TensorRT

ONNX

OpenVINO

Kubernetes

GCP

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.

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