Computer Vision/Deep learning engineer Offline

Requirements:

— Technologies:

Mediapipe framework (Skeleton models and behavior recognition)

yolov5

CNNs

Torch

Python

OpenCV

Experience with models on edge devices

— Experience:

2+ years in CV applications;

previous experience in building high load computer vision applications;

previous experience in training and deploying of light models on EDGE device;

models distillation.

It will be a great bonus:

— Prior knowledge of C/C++/C#/Go as second language;

— Experience of development and deploying large scale distributed computer vision applications;

— Experience of development and deploying large scale distributed projects with Machine and Deep learning models;

— Experience with PyTorch and/or Tensorflow or other ML/DL libraries, (DLIB, mediapipe, scipy);

Responsibilities/about project:

— Leading the Computer vision team for model's selection, training, and finetuning,

— Searching/designing and preparing datasets for models training,

— Research, Design, development, and training of Computer Vision models for the following modules:

Identification of guns, pistols, knifes in video feeds,

People detection and recognition:

use case: face recognition - if the person was previously caught on scamming,

use case: more than 1 person present near the screen of ATM,

Behavior recognition models:

use case: person in the video frame is trying to fix/scam ATM device,

use case: fight detection,

— Participating in architectural design of a highly efficient modular platform for distributed processing of raw and compressed video feeds from 21k-200k ATMs worldwide.

— Participating in the process of testing of trained models in the fields and incremental quality improvement.

— Interaction with the product owner.