Deep Learning Engineer

Description:

- Strong programming skills in Python and/or C++

- Experience with deep learning frameworks such as TensorFlow, PyTorch, Keras, and/or MXNet

- Experience with computer vision libraries such as OpenCV, dlib, and/or OpenVINO

- Familiarity with object detection algorithms such as YOLO and Faster RCNN

- Experience with object segmentation and pose estimation models such as Mask R-CNN, U-Net, and/or OpenPose

- Experience with Recurrent Neural Networks (RNNs)

- Experience with Fully Convolutional Networks (FCNs)

- Experience with Variational Autoencoders (VAEs) and/or One-shot Learning Networks,

- Experience with generative adversarial network (GAN) models

- Experience with deploying deep learning models on edge devices such as NVIDIA Jetson, Raspberry Pi, and/or Google Coral

- Experience with deploying deep learning models using NVIDIA TensorRT and/or NVIDIA Riva

- Knowledge of natural language processing techniques and frameworks such as Rasa

- Experience with web development frameworks such as Flask for building APIs

- Knowledge of feature engineering and data preprocessing techniques for machine learning

- Familiarity with deep learning model optimization techniques such as pruning, quantization, and compression

- Experience with cloud computing platforms such as AWS for deploying and scaling deep learning applications

- Familiarity with deep learning model interoperability formats such as ONNX

- Experience with CUDA programming for GPU acceleration

- Familiarity with deep learning frameworks such as Caffe and/or - - -- Docker for model development and deployment

- Familiarity with object tracking algorithms such as Deep SORT, SORT, and Kalman filters

- Experience with container orchestration systems such as Kubernetes, which can help manage and deploy containerized applications at scale.

- Experience with continuous integration/continuous delivery (CI/CD) pipelines for automated testing and deployment of software applications.