AI in IT Jobs: Navigating Opportunities and Skills for Success

Artificial intelligence (AI) is becoming a greater part of IT jobs. The growing popularity of this technology means additional skills are needed to fill the increasing opportunities in the IT industry.

AI includes machine learning, natural language processing, data mining, image recognition, robotics, and decision-making algorithms. These technologies require computer science, math, engineering, and related skills.

The increasing use of AI elevates the demand for these skills. Working with this technology can lead to ongoing opportunities for career development.

Learn how to navigate AI opportunities in IT jobs and the skills needed for success.

Data Skills

Working with data in AI projects involves data extraction, data analysis, and data visualization. Therefore, knowledge of programming languages such as Python, R, and SQL and experience with tools such as Pandas and NumPy are required.

Machine Learning Skills

Machine learning analyzes data to uncover patterns, create predictions or judgments, and learn from experience. Proficiency in programming languages such as Python, R, and MATLAB and experience with machine learning frameworks such as TensorFlow and Keras are needed to create the algorithms.

Natural Language Processing Skills

Natural language processing analyzes text to understand human language and produce natural, believable answers. Programming skills in Python and Java and familiarity with natural language processing tools such as NLTK and spaCy also are essential.

Machine Learning Engineer

A machine learning engineer uses software, predictive models, and natural language processing to analyze data sets. This professional understands software development methodology, agile practices, software development tools from integrated development environments such as Eclipse and IntelliJ, and the components of a continuous deployment pipeline.

Robotic Scientist

A robotic scientist builds mechanical devices that perform tasks with commands from humans. Writing and manipulating computer programs, collaborating with other specialists, and developing prototypes are required.

Data Scientist

A data scientist collects data, then uses machine learning and predictive analytics to analyze and interpret the data. Expertise in using big data platforms and tools such as Hadoop, Pig, Hive, Spark, and MapReduce is required. Fluency in statistical computing languages and programming languages such as SQL, Python, Scala, and Perl also is essential.

Research Scientist

A research scientist is an expert in machine learning, natural language processing, computational statistics, applied mathematics, and other AI disciplines. These professionals use deep learning, graphical models, reinforcement learning, computer perception, and data representation in their work.

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Engineering Interviews: Common Questions and How to Nail Them

Before participating in engineering interviews, you should practice answering common questions. Developing clear, concise answers to typical interview questions helps show the interviewer how your knowledge, skills, experience, and accomplishments equip you for the role.

Preparing for engineering interview questions helps increase your confidence level. The more confident you feel, the more likely you will advance in the hiring process.

Practice answering these common questions to prepare for your engineering interview.

What was the most difficult project you worked on, and how did you get through the challenging tasks?

Show how you manage your time and multitask while working on complex projects that require multiple teams, resources, and timelines. Include your thought processes for overcoming challenges and your skills used to navigate forward.

For instance, “I worked on an engineering project that involved making predictions about mountains and the nearby roads. I changed the data to include climate and environmental changes so the risk factors aligned with the wildlife, weather, and residents. Although the project was challenging in the beginning, making these changes helped me complete it.”

How do you explain engineering ideas to an audience who is unfamiliar with the topic?

Demonstrate that you have a strong understanding of engineering concepts and can clearly explain them to non-engineering professionals. For instance, “I provide examples of engineering that are easily understood and relate to the person or situation. For example, when I talk with construction workers, I compare what I am saying to the process of how roads are paved. Relating the engineering ideas to real-life situations provides a better understanding of them.”

Which resources do you use to stay current with engineering developments?

Clarify the engineering blogs, magazines, professional associations, or other resources you use to learn about the latest news, technology, and trends. Also, demonstrate your ability to adapt to new processes and best practices.


For instance, “I subscribe to authoritative engineering journals and actively participate in engineer-based Slack chat rooms. I also read blogs and articles on the latest research and stay connected with peers outside of work.”

Which safety procedures are in place for when you check your engineering work for mistakes?

Show how seriously you take safety measures when working to minimize potential issues. Include how your knowledge of safety standards supports your desire to keep projects safe.

For instance, “I constantly check for mistakes. For example, I triple-check my math and measurements and ask a fellow engineer to check my work before marking it complete. These steps help ensure there are no mistakes.”

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