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What Does an Artificial Intelligence Engineer Do?

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June 3, 2021

  • AI Engineer
  • AI Scientist
  • AI Interaction Designer
  • Computational Scientist
  • Computer and Information Research Scientist
  • Computer Systems Designer
  • Software Developer
  • Establishing and achieving objectives using techniques associated with AI reasoning and uncertainty.
  • Applying logic, probability analysis, and machine-learning concepts to problem-solving initiatives.
  • Analyzing systems to effectively monitor and control development projects.
  • Using AI best practices in regards to applications in speech recognition, data processing, data mining, and robotic control.
  • AI is a broad term that describes applications where a machine mimics human cognitive functions like learning and problem-solving. An AI system can be incredibly complex, or as simple as a series of nested if-else statements.
  • Machine learning utilizes classical algorithms to complete tasks, like clustering, regression or classification, and machine learning algorithms must be trained on data. The more they’re trained, the better they perform.
  • Transportation – Self-driving cars are a potential game changer, and they’re literally driven by AI and machine learning technology.
  • Manufacturing – AI-driven robots offer increased efficiency, accuracy, etc., sometimes dramatically improving production speeds and profits.
  • Healthcare – AI and machine learning tech powers autonomous surgical robots, virtual nursing assistants, automated image diagnosis, and dosage error reduction.
  • Entertainment – Machine learning technology is used to predict user behavior and custom-tailor suggestions for movies, music, TV shows, and even advertisements.
  • Sports – Automation and predictive analysis technology is used to drive business decisions, sponsorship activations, ticket sales, and even to forecast athletic performance.
  • Analyzing and associating AI principles into reasoning and uncertainty in any perspective environment.
  • Applying AI and machine learning techniques for image analysis and reconstruction.
  • Implementing AI and machine learning solutions to solve a variety of complex problems or scenarios.
  • Developing AI-driven solutions that model human behavior to accomplish complicated tasks or complete complex processes.
  • Creating solutions that combine artificial intelligence best practices with machine learning principles.
  • Evaluating and improving the performance of applications in artificial intelligence and machine learning domains.
  • Deep-learning libraries, such as Tensorflow.
  • A proficiency programming in Python.
  • Experience applying AI solutions to solve complex problems in healthcare, manufacturing, oil/gas, and automotive.