AI engineers are in high demand and for good reason. Artificial intelligence has seemingly endless potential to improve and simplify tasks commonly done by humans, including speech recognition, image processing, business process management, and even the diagnosis of disease. If you’re already technically inclined and have a background in software programming, you may want to consider a lucrative AI career and know about how to become an AI engineer.
What is Artificial Intelligence?
Artificial Intelligence (AI) is a computer system’s ability to mimic human behavior. Machines demonstrate this sort of intelligence, which can be compared to a natural intelligence that humans and animals demonstrate.
In artificial intelligence, machines learn from past data and actions, which are positive or negative. With this new information, the machine is able to make corrections to itself so that the problems don't resurface, as well as make any necessary adjustments to handle new inputs. Finally, the machine is able to perform human-like tasks.
Let us understand what an AI engineer does in the next section of How to become an AI Engineer article.
What Does an AI Engineer Do?
An AI engineer builds AI models using machine learning algorithms and deep learning neural networks to draw business insights, which can be used to make business decisions that affect the entire organization. These engineers also create weak or strong AIs, depending on what goals they want to achieve.
AI engineers have a sound understanding of programming, software engineering, and data science. They use different tools and techniques so they can process data, as well as develop and maintain AI systems.
The next section of How to become an AI Engineer focuses on the responsibilities of an AI engineer.
Responsibilities of an AI Engineer
As an AI engineer, you need to perform certain tasks, such as develop, test, and deploy AI models through programming algorithms like random forest, logistic regression, linear regression, and so on.
- Convert the machine learning models into application program interfaces (APIs) so that other applications can use it
- Build AI models from scratch and help the different components of the organization (such as product managers and stakeholders) understand what results they gain from the model
- Build data ingestion and data transformation infrastructure
- Automate infrastructure that the data science team uses
- Perform statistical analysis and tune the results so that the organization can make better-informed decisions
- Set up and manage AI development and product infrastructure
- Be a good team player, as coordinating with others is a must
Skills Required to Become an AI Engineer
Professionals who are finding how to become an AI engineer should also know about the skills required in this field. Some of them include:
The first skill required to become an AI engineer is programming. To become well-versed in AI, it’s crucial to learn programming languages, such as Python, R, Java, and C++ to build and implement models.
Linear Algebra, Probability, and Statistics
To understand and implement different AI models—such as Hidden Markov models, Naive Bayes, Gaussian mixture models, and linear discriminant analysis—you must have detailed knowledge of linear algebra, probability, and statistics.
Spark and Big Data Technologies
AI engineers work with large volumes of data, which could be streaming or real-time production-level data in terabytes or petabytes. For such data, these engineers need to know about Spark and other big data technologies to make sense of it. Along with Apache Spark, one can also use other big data technologies, such as Hadoop, Cassandra, and MongoDB.
Algorithms and Frameworks
Understanding how machine learning algorithms like linear regression, KNN, Naive Bayes, Support Vector Machine, and others work will help you implement machine learning models with ease. Additionally, to build AI models with unstructured data, you should understand deep learning algorithms (like a convolutional neural network, recurrent neural network, and generative adversarial network) and implement them using a framework. Some of the frameworks used in artificial intelligence are PyTorch, Theano, TensorFlow, and Caffe.
Communication and Problem-solving Skills
AI engineers need to communicate correctly to pitch their products and ideas to stakeholders. They should also have excellent problem-solving skills to resolve obstacles for decision making and drawing helpful business insights.
Let us explore the career and roles in AI in the next section of the How to become an AI Engineer article.
AI Engineer Salary
According to Glassdoor, the average annual salary of an AI engineer is $114,121 in the United States and ₹765,353 in India. The salary may differ in several organizations and with the knowledge and expertise, you bring to the table.
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How to build a Career in AI
Since several industries around the world use AI to some degree or the other, including healthcare and education, there has been exponential growth in the career opportunities within the field of AI. Some of these job roles are:
An AI developer works closely with electrical engineers and develops software to create artificially intelligent robots.
AI architects work closely with clients to provide constructive business and system integration services. They also create and maintain the entire architecture.
Machine Learning Engineer
Machine learning engineers build predictive models using vast volumes of data. They have in-depth knowledge of machine learning algorithms, deep learning algorithms, and deep learning frameworks.
Data scientists collect, clean, analyze, and interpret large and complex datasets by leveraging both machine learning and predictive analytics.
Business Intelligence Developer
They're responsible for designing, modeling, and analyzing complex data to identify the business and market trends.
How Can Simplilearn Help You?
Simplilearn has teamed up with IBM to offer the Artificial Intelligence course especially for professionals willing to know about how to become an AI engineer. This program consists of a number of valuable courses, such as:
- Introduction to Artificial Intelligence
- Data Science with Python
- Machine Learning
- Deep Learning Fundamentals by IBM
- Deep Learning with TensorFlow
- AI Capstone Project
After completing this program, you'll earn certificates from both IBM and Simplilearn, which are recognized worldwide. IBM is the second-largest predictive analysis and machine learning solutions provider globally.
A joint partnership with Simplilearn and IBM introduces you to integrated blended learning, making you an expert in AI and data science. This Master’s Program, co-developed with IBM, will make you industry-ready for AI and data science job roles. Additionally, you can get the following:
- Job-seeking assistance from Simplilearn
- Experience with more than 15 real-life projects
- Assistance with over 20 in-demand skills and tools, such as NumPy, SciPy, Apache Spark, Watson, Keras, Python, and R
- Access to exclusive forums moderated by expert faculty and industry thought leaders
- $1,200 worth of IBM cloud credits that you can leverage for hands-on experience
- Access to IBM cloud platforms, such as IBM Watson (IBM's own supercomputer) and other software for 24/7 practice
- Access to digital badges from IBM
- The industry-recognized Master Certificate from Simplilearn
Now that you have understood how to become an AI engineer, get started on your journey to becoming a certified AI Engineer and landing your dream job!