LR109 Deep Learning Architect I

Deep Learning Architect I, Cloud Computing Technologies Agile Development Labor Rate develops deep learning architectures for self-learning systems and applications. Verifies data quality and ensure data acquisition to get complete picture of the information needed. Identifies errors in the designed deep learning models to and capable of designing strategies to resolve them. Visualize and manipulate big and complex datasets. Ensures that the deep learning solutions deployed by an organization meet current requirements and are capable of evolving to meet future needs. Deals with the complexities that come with organizational artificial intelligence assets. Choose appropriate hardware for deploying machine learning models with the required efficiency. Develop software architectures used in developing robots and other artificial intelligence systems. May have certifications in technical courses such as computer science, robotics, mathematics, engineering, artificial intelligence, and so on. Must have 2-5 years of satisfactory experience in high-pressure deep learning positions or equivalent certification in lieu of years of experience. May Proficient in Linux and Unix operating systems and in programming languages such as Java, LISP, Python, C, Perl, MetLab. Possesses excellent creative, communication, social, and problem-solving skills. Able to work with a team of other professionals in similar fields such as software programmers or developers, deep learning and machine learning experts, artificial intelligence professionals, and data scientists. Stay abreast of latest developments in the artificial intelligence industry, decide on the need for new technologies and develop effective timeline for adopting new technologies. Liase with relevant teams in building deep learning algorithms that use data to train models which are used in automating systems such as image recognition, market forecasting, sales automation, speech recognition, data analytics, image classification systems, and so on. Communicate with various groups of end users to identify their pain points and develop tailor-made solutions for each problem by analyzing necessary data so as to select the best algorithms for each context.

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