As AI brain drain grows, so does the need for AI Engineers. And due to the recent interest of companies in AI, it’s a fairly high-paid job. “AI Engineer”. In this article, we will go through everything you need to know to become an AI Engineer.
First things first, what is AI?:
AI, short for, Artificial Intelligence, is the branch of computer science that deals with the replication of the human mind. There are three types of AI: Weak AI, Strong AI, and Hypothetical AI.
What exactly does an AI Engineer do?
An AI Engineer’s job is to build AI models that use machine learning algorithms and deep learning neural networks to carry out specific tasks. An AI Engineer can work on either weak or strong AI. It all depends on what they’re striving towards.
AI Engineers need to be good at programming, and software engineering. They must also have a clear understanding of data science. AI Engineers utilize various tools and techniques to process data to develop functional AI systems.
What things should an AI Engineer be good at?
An AI Engineer must be able to perform certain tasks like developing and testing an AI. He/She must know how to deploy the AI via programming algorithms. Some examples of programming algorithms are logistic regression, linear regression, and random forest.
A few other things an AI Engineer must excel at are:
- Converting machine learning models to application program interfaces (APIs) so other applications can utilize them.
- Building AI models from scratch, and being able to help product managers understand what benefits they gain from their model.
- Building data ingestion and data transformation infrastructure.
- Automating infrastructure utilized by the data science department.
- Carrying out statistical analysis, and helping their company make informed decisions more accurately by tuning the results.
- Setting up and managing AI development and product infrastructure.
Apart from that, there’s one thing you should keep in mind. We’re certain you already know this, but you need to be a good “team player”. Coordination is a must in a job like this.
Skills required to be an AI Engineer:
Certain skills are a must for becoming an AI Engineer. You shouldn’t just learn them, but excel at them as well. The skills required are:
- The first and most important one would be programming. An AI Engineer must learn several programming languages to work efficiently or be able to apply for the job. A few examples of programming languages are Python, Java, and C++.
- An AI Engineer must excel in certain fields of mathematics. The main ones are Linear Algebra, Probability, and Statistics. Excel in these fields is crucial as an AI Engineer needs to utilize them to implement different AI models.
- An AI Engineer must be aware of various spark and big data technologies. The reason is that this job requires working with large volumes of data, whether its streaming or real-time production-level data (in terabytes or petabytes). Some examples are Hadoop, Cassandra, and MongoDB, and Apache Spark.
- The second most important skill would be understanding Algorithms and Frameworks, and being able to implement them into machine learning models. Some examples of algorithms are linear regression, KNN, and Support Vector Machine. Understanding deep learning algorithms is also an essential part of the job (a few examples can be a convolutional neural network, recurrent neural network, and generative adversarial network). They must also know how to implement frameworks correctly.
In the next part of the article, we’ll go through two career choices. There are various options because the use of AI has expanded a lot over the years, and now almost every country utilizes AI, oftentimes to get the upper hand against its competitors. A few of them are:
#1: AI Developer
This field requires developing software to create AI while working closely with electrical engineers.
#2: AI Architect
This field requires working closely with clients to provide constructive business and system integration servers while also creating and maintain the architecture.
#3: Machine Learning Engineer
This field requires working with large volumes of data to create predictive models. A clear understanding of machine learning algorithms, deep learning algorithms, and deep learning frameworks is required for this field.
#4: Data Scientists
This field requires excelling at collecting, cleaning, analyzing, and interpreting large and complex datasets (utilizing both machine learning and predictive analytics).
#5: Business Intelligence Developer
This field requires designing, modeling, and analyzing complex data to identify business and market trends.
In this article, we have gone through everything you need to know about becoming an “AI Engineer”. You should also keep in mind that a Ph.D. or a Masters degree at the least is required to apply for this job. We hope this article answers all your queries, but if you have any further questions, or would like to change/add something to this article, feel free to comment down below!