Machine Learning Architect Offline

Your skills and expertise:

• MS/PhD degree in computer science or related

• Deep knowledge and proven practical experience in a relevant field of research, such as machine learning, computer vision, speech processing, natural language processing, and data science

• Solid Experience architecting and developing AI and machine learning applications

• Strong understanding of machine learning algorithms and deep networks (CNN, DBN, RNN, LSTM, DCN)

• Proficiency in SOLID and Systems Architecture design approaches

• Strong knowledge in machine learning fundamentals i.e. regression models, decision trees, naïve Bayes, clustering algorithms (k-means, DBSCAN, SOM), dimensionality reduction (PCA, t-SNE) and a good grasp of the strengths and weaknesses of specific approaches. A good foundation in basic statistics and linear algebra

• Strong Python knowledge

• Experience implementing DL algorithms in high-level languages (e.g. Python, C++)

• Experience using machine learning toolboxes and libraries (e.g. TensorFlow, PyTorch, Keras or MXNet)

• Comprehensive knowledge of the Python data analyses ecosystem (Pandas, Numpy, Scikit-learn, etc.)

• At least minor experience with python visualization tools (matplotlib/seaborn, Plotly)

• Experience with following modern neural network architectures: LSTM and other RNN-based, ResNet and other CNN-based, Autoencoders, U-Net, GANs and VAE

• Superior presentation, communication, and interpersonal skills

• Accomplished estimation and people motivation skills

• Ability to talk both technical language and the language of business stakeholders

• Upper-intermediate level of English mandatory

Will be a plus:

• Experience with R, C++

• Familiarity with time-series predictive/anomaly detection analyses, natural language processing, signal processing

• Understanding SOTA approaches for machine learning problems like unsupervised/ semi-supervised learning.

• Experience with the following DL frameworks: DLib, Darknet, Theano

• Awareness of CRISP-DM process model

• Experience with continuous integration and release management tools, preferably within the AWS platform.

• Hands-on Experience with the common architecture of MLOps system by the means of Hadoop, Docker, Kubernetes, cloud services and experience with managing production ML lifecycle

• Experience with distributed training on GCP

• Experience with full stack applications with Microsoft Azure platform is a big plus