Triplet loss for multi-label classification using Deep Learning & Deep Neural Networks

looking for an expert in deep learning neural network, machine learning and python, who is knowledgeable in triplets network, alexnet, ResNet architecture and python libaries, training the network and extracting key features and who also has solid background in SVM, image processing, face detection and recognition. This will be implemented using Keras library with Theano or Tensorflow backend. - must be capable of training and fine tuning the network using triplet loss cnn (Alexnet) neural network, extracting features for further training - must be capable of not only replace and retrain the classifier on top of the ConvNet on the new dataset, but to also fine-tune the weights of the pretrained network by continuing the backpropagation. It is possible to fine-tune all the layers of the ConvNet, or it’s possible to keep some of the earlier layers fixed (due to overfitting concerns) and only fine-tune some higher-level portion of the network. - must be capable of building and compiling of python libraries, python in linux (ubuntu) - train and test models and improve the accuracy and reduce the loss of the classification results in imagenet models including (Alexnet), ResNet and etc..... So the main idea of the triplet loss is to separate embeddings of the positive pair (anchor and positive) from embeddings of the negative pair (anchor and negative) by a distance margin M.