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

📌 Computer Vision (Capital)
🏢 Wakapi
📍 Capital