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 andprocessing 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 executionon 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 deeplearning models in production environments.

- Deep expertise with modern object detection architectures (YOLO, DETR, FasterR‑CNN, Vision Transformers).

- Strong programming skills in Python and deep learning frameworks (PyTorch,OpenCV, TensorFlow).

- Proven experience working with video stream ingestion, frame processing, andlow‑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 forhardware acceleration.

YOLO / DETR | Computer Vision | PyTorch / OpenCV | Video Stream Processing | LPR / OCR |

Edge & Cloud Inference

📌 Computer Vision (Capital)
🏢 Wakapi
📍 Capital