LR088 Senior Deep Learning Scientist

Senior Deep Learning Scientist, Cloud Computing Technologies Agile Development Labor Rate responsible for the development of deep learning algorithms and Deep Neural Networks that enable artificial intelligence technologies to simulate human intelligence that such as those that allow unmanned aircrafts to perceive and understand their environment. Solve various problems involving the perception, prediction, planning, and control of artificial intelligence technologies to achieve a level of autonomy. Improve existing deep learning algorithms and collaborate with software engineers in the development of deep learning-enabled applications that bring about increased efficiency, productivity, and profits. Develop AI algorithms that is capable of providing insights and predictions that influence business or organizational decisions. Keep abreast of the latest developments in the world of deep learning algorithms and endeavor to stay a step ahead of the competition for the benefit of the organization. Ensure that deep learning algorithms and the resulting artificial intelligence product or applications are well-evaluated, and adequate tests are done on them before deployment. May have a Master’s Degree or Ph.D. degree in Computer Science, Applied Mathematics, Robotics, or other related fields; with 11 or more years of working experience with deep learning models and working with deep learning tools and frameworks. Proficient in the knowledge of deep learning such as TensorFlow, Theano, Sonnet, Keras, Gluon, MxNet, PyTorch, and so on. Proficient in a variety of programming languages such as Python, Lisp, Java, Prolog, C++, and so on. Proficient in solving complex analytical and mathematical problems and possess a strong knowledge of software architecture optimization. Have excellent communication and collaboration skills to work with various teams in a fast-paced, ever-dynamic environment. Develop self-learning microservices applications that are able to work independently to simplify organizational workflow, increase organizational efficiency, and reduce production costs.

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