What Do AI Engineers Really Do? Daily Roles & Responsibilities

June 3, 2021

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鈥檙e trained, the better they perform.

Transportation – Self-driving cars are a potential game changer, and they鈥檙e 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.

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