Deep Learning Consultant, Cloud Computing Technologies Agile Development Labor Rate helps various groups of software development teams with the leveraging of a variety of data acquired from users to help develop deep learning models and technologies. Work together with the research team to discover new deep learning approaches and involved in the implementation of these models and strategies. Identify model drifts for deep learning applications end users and help them and train them on the functionalities and operations of these models after they are built. Maintain frequent communication with users, accurately analyze data gotten from them to understand their needs, then design and develop models to meet their needs from start to the end. Ensure that the validation of the models is adequately carried out to ensure that requirements are met. Help end-users (usually large organizations) meet their needs concerning deep learning, machine learning, artificial intelligence, and automation. Assist the data analysis team is working on high-impact projects that will help not just present business strategies but also long-term ones. Make use of artificial intelligence to create transformational changes that lead to large scale breakthrough solutions in different industries. Make various predictive analytic models and deep learning models available for use in every industry for solving a variety of complex problems. May have a Master’s degree or Ph.D. in Computer Science, Physics, Electrical Engineering, Statistics, or equivalent discipline with at least 10 years of experience as a deep learning expert. Demonstrate expertise in Software Engineering, Database programming, Applications development, and data analytics. Have fluency of a variety of programming languages such as Python, JavaScript, C++, SQL, MS SQL, HTML, and so on. Have excellent communication and collaboration skill that enhance synergy within the deep learning consultant team and with a wide base of end users. Keep up with researches in the deep learning niche by collaborating with academics, reading research publications to update knowledge, and attending conferences or seminars with other deep learning experts. Ensure that all newly developed deep learning algorithms and technologies are capable of meeting current and future needs.
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