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What Does an AI Engineer Do?

by Eric
AI Engineer

The designation of Artificial Intelligence Engineer or AI Engineer is becoming popular with every passing year now. The career path to becoming one such professional includes formal training to develop an in-depth understanding of AI-related topics. You may think about what kind of career path it must be and what skills can lead to an AI engineer job. But first off, you need to know who an AI engineer actually is. Basically, AI engineers develop, program, and train complex networks of algorithms that constitute AI so that they can mimic the human brain. An artificial intelligence engineer wannabe is expected to be well-versed in programming, software development, data engineering, and data science.

So how does one start learning AI? Well, by achieving a certification wherein one can master areas like machine learning, data science, Python programming, deep learning, and natural language processing. Taking such a step will help become a part of the scarce AI talent and command high salaries in whichever company you land a job in. Being an AI engineer is definitely a rewarding career and its demand doesn’t seem to slow down any time soon. With AI impacting all businesses and industrial sectors, you must ensure career success with an online AI engineer course.

But before you start your journey to become an Ai engineer, you must be familiar with the responsibilities associated with such a role. Understanding what an AI engineer does on a daily basis in an organization will help you decide whether your career interests align with the role and plan your training accordingly. Read on to find out what companies expect from their AI engineers.

Responsibilities Handled by AI Engineers

The primary task of an AI engineer is to develop AI models using ML algorithms and deep learning neural networks to draw meaningful conclusions, which can be used for effective decision-making. They identify and source data from multiple sources to build and test ML models. They also implement AI-powered applications by making use of embedded code or application programming interfaces (APIs).

Here are some of the responsibilities handled by AI engineers in different organizations.

  • They develop machine learning applications based on business requirements.
  • They design the data ingestion and data transformation infrastructure.
  • They conduct statistical analysis and interpret the results to facilitate better decision-making.
  • They transform machine learning models into APIs so that other applications can work with them.
  • They help stakeholders and product managers understand the value of AI models and how they can benefit the organization.
  • They run AI and machine learning experiments and tests as and when required.
  • They are responsible for automating the infrastructure used by the data science team.
  • Coordinating with the robotics team, data science team, and engineering team is also one of their responsibilities.

How to Reach the AI Engineer Designation?

Now that you are aware of what AI engineers do, you must know how to prepare for such a role. First off, we should know about the educational background. It is recommended to have at least a bachelor’s degree in mathematics, robotics, computer science, information technology, or other related engineering domains. Then you should become proficient in areas like data science, machine learning, software development, robotics, electronics, and object-oriented programming. Read more about Types of Deep Learning Networks.

It is important to brush up on your core math knowledge before starting with AI. Linear algebra, statistics, probability, and calculus are some of the topics you should have a detailed knowledge of. Next, AI engineers work with massive amounts of data and so it is necessary to be familiar with big data technologies like Hadoop, Cassandra, and Spark. Another useful topic related to AI engineering is the understanding of machine learning algorithms like KNN, support vector machine, Naive Bayes, linear regression, clustering, and more. Frameworks like Theano, Caffe, PyTorch, and TensorFlow are some of the frameworks that come in handy for AI engineers.

When it comes to soft skills, wannabe AI engineers are expected to think critically, analytically, and creatively. They should have the ability to solve problems in real-time, identify trends quickly and draw insights based on the findings, and question established business practices. Business acumen and industry knowledge are also must-have skills, along with the ability to work in a supportive and collaborative work environment.

Now, if you are a working professional, the number of topics to gain expertise in may seem overwhelming and you may drop off your decision to become an AI engineer. Before you can think so, here is the good news – there are online AI courses to help you learn everything from scratch. All the study materials will be provided to you and you just need to start with the detailed syllabus. All the topics are covered step by step, and there are industry experts with rich teaching experience to explain everything. Moreover, the videos are byte-sized, meaning they are short and need only a few minutes to complete. This way, such training modules can be easily squeezed into your busy schedule.

So, why wait when there is a rewarding career option right in front of you. Take up a reliable AI training course and gain employable skills right away.

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