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