Top Skills Needed to Become an AI Engineer
Companies know all too well how hard it is to find and hire a qualified software engineer, especially in emerging fields like artificial intelligence (AI). Job hunters are also frustrated because they don’t know exactly what companies are looking for.
So, what are the top skills which companies needs and candidate should master to become AI engineers? Here are the top technical skills and soft skills top engineers have:
The top technical skills for AI engineers include:
Python is the most widely used programming language for AI and is widely used for machine learning, natural language processing, and neural network connections.
However, Python is just one of the programming languages used. Some of the more popular languages used are:
Algebra, Probability, and Statistics
AI engineers must understand the different types of AI models. That requires a firm grasp of linear algebra, probability, and statistics.
Common AI models include:
- Linear regression
- Logic regression
- Decision trees
- Linear discriminant analysis
- Naïve Bayes classifiers
- Support Vector Machines
- Learning Vector Quantization
- K-nearest Neighbors
- Random Forest
Big Data Analysis
AI can incorporate massive data sets. AI engineers must be well-versed in the tools and processes for managing these large data sets. A background in Apache Spark architecture, Hadoop, Cassandra, and MongoDB will help.
Algorithms make many decisions within AI frameworks that are necessary to drive machine learning models. Deep learning algorithms, especially for unstructured data, require a framework for working. AI frameworks include tools such as PyTorch, Theano, TensorFlow, and Caffe.
Going beyond the technical skills, however, employers are also looking for the soft skills that make AI engineers a fit for the workplace. This includes such skills as
Problem-Solving and Critical Thinking
Software development is about solving complex problems.
For AI engineers, the ability to handle problems and solve them is an essential skill. This requires critical as well as creative thinking, and the flexibility to examine different solutions to determine the best path forward.
AI teams often work both independently and together at different times during development. Communication is an essential function, especially if team members are working remotely. This requires both written and verbal communication skills and, depending on the project and organization, may require interfacing with clients or stakeholders.
Most people that you will be required to communicate with, will not have the technical skills or understanding of AI concepts. So, the AI engineers would be required to break down complex ideas into simplified language that can be understood by team members belonging to non-technical backgrounds.
An AI engineer is required to be able to work with team members effectively. This means displaying soft skills such as patience and empathy to help create productive interactions. This is especially crucial when AI engineers need to collaborate on projects.
It also means the ability to listen to others’ ideas with judgment and be willing to accept other solutions to problems besides just their own.
Self-awareness is the ability to see yourself as others see you. This helps you judge how your actions will affect others and interpret actions, thoughts, and feelings objectively. While self-confidence is an important soft skill, so is the ability to admit a weakness and seek help to fill in missing skills gaps.
A survey by Stack Overflow found that 87% of software engineers responding to its survey had taught themselves a new technology or programming language. Three-quarters of software engineers and developers say they learn new skills every few months or at least once a year.
In emerging tech fields, continuous education is an essential skill. AI developers must be self-motivated and curious to stay on top of emerging trends and learn what’s needed to remain current.
AI engineers are working on projects with deadlines and that are time-bound in most cases. Strong time management skills are essential to staying focused to meet milestones for deliverables.
In a rapidly evolving tech environment, the ability to adapt to changes and different ways of doing things can make a big difference. As more knowledge is gained, specifications change, or new tech evolves, AI engineers must be able (and willing) to adapt to new approaches.