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
The CHI team is looking for a Machine Learning Architect.
Our client — Shipping & Transportation/USA.
Project — The RPA solution for the worldwide company offers industry-leading shipping services, including transportation, warehousing, distribution, custom brokerage, and logistics management solutions.
The RPA solution for the worldwide company offers industry-leading shipping services, including transportation, warehousing, distribution, custom brokerage, and logistics management solutions.