Artificial Intelligence (AI) is a technology that has become increasingly popular around the world due to its potential to transform organizations, economies, and the society we live in. AI has influenced nearly every industry, education, financial, manufacturing, healthcare, gaming, and others. Businesses from different sectors are leveraging AI to build cutting-edge solutions for their customers or for their own in-house use to make business operations more efficient and overcome any problems. Just as the demand for AI in organizations is increasing, there is tremendous scope for a career in AI.
- The Artificial Intelligence market is expected to grow from $86.9 billion in 2022 to $407 billion by 2027.
- By 2025, it is expected that there will be 97 million new AI jobs.
If you are planning to build a career in AI, now is the best time to start. An AI career path will give you a range of new opportunities to explore and work with leading organizations from around the world. However, to become an AI engineer or to pursue any role in AI, you require certain AI skills. These AI skills can be categorized into technical and non-technical.
Technical skills
Programming language
- The programming language is a language of computers that helps them effectively communicate with various systems and processes.
- The programming language allows an individual to provide a standard set of instructions to the computer to perform a specific function and activity.
- To have a career in AI, the aspirant must have knowledge and proficiency in various programming languages, such as JavaScript, C++, R, Java, Python, etc.
- These programming languages will help an AI engineer write code depending on the requirements and use cases.
AI frameworks and ML libraries
In addition to the programming languages, AI professionals must also have an idea of how various frameworks and libraries function. This will enable them to choose the framework and library that best fit the project requirements. Some of the popular frameworks and libraries that an AI engineer needs to know about are:
- TensorFlow
- Apache Spark
- Pytorch
- Keras
- Pandas
- Scikit-learn
- Caffe
- Net
Mathematics concepts
All AI systems and data science systems consist of mathematical equations. An AI engineer works extensively with applied mathematics and algorithms. AI jobs involve a lot of problem-solving for which knowledge of mathematics, statistics, and basic analytics is needed. For an AI career path, aspirants must have knowledge of the following:
- Linear algebra
- Differential equations
- Probability
- Calculus
- Graphs
- Statistics
- Linear regression
- Matrix
- Numerical analysis
- Applied math
Distributed computing
Most AI jobs involve handling large amounts of data, which cannot be processed on a single computer. The data needs to be distributed evenly on various networked computers. Hence, those planning a career in AI must improve their efficiency and knowledge of distributed computing.
Big Data
- With the large number of datasets in AI, a single system will not be sufficient.
- Big Data is a technology for extracting, managing, and analyzing huge volumes of data efficiently.
- Big Data tools help an AI engineer derive and analyze valuable insights from massive datasets.
- Hence, familiarity with various Big Data tools is essential.
Knowledge of artificial neural networks
- Artificial neural networks refer to a set of algorithms or a system that can replicate the functioning of the human brain.
- The neural networks are used for data processing, predictive modeling, pattern recognition, face recognition, regression analysis, etc.
- In today’s AI-driven world, an AI engineer needs to have a basic understanding of neural network architecture.
Signal processing techniques
- An AI engineer must know how to solve problems using signal processing algorithms.
- Professionals in AI jobs who have the knowledge and expertise in signal processing techniques can achieve increased productivity and efficiency in digital communication.
- Knowledge of advanced signal processing techniques would be an added advantage in AI jobs.
Non-technical skills
Domain expertise
- A career in AI requires AI engineers to have expertise in the domain in which they are working.
- Organizations expect individuals to have complete domain knowledge for industry-specific solutions.
- For example, if an AI engineer is developing AI solutions for a vaccine firm, then a basic understanding of vaccination, vaccines, and immunology would be preferred.
Clear communication skills
- A career in AI will more often than not, require AI professionals to communicate with other teams and collaborate with them.
- AI jobs also require communication with stakeholders.
- Clear communication skills are indispensable for a career in AI to explain complex AI topics and findings to people from a non-technical background.
Decision-making
Decision-making skills are crucial for an AI engineer. A career in AI involves making the right decisions at the right time for the success of a project. The AI engineer must take decisions to minimize the risks and ensure the client and end users are happy and satisfied.
Problem-solving approach
If you do not have this skill, it is highly unlikely that you will survive or grow in a career in AI. You need to identify and solve complex problems with analytical thinking in your AI career path.
In addition to these skills, you also require some other non-technical AI skills. These include:
- Creativity
- Collaboration
- Interpersonal skills
- Time management
- Systematic thinking
- Leadership and management skills
Start working on these technical and non-technical skills today to build your career in AI. These AI skills will definitely help you begin your AI career path smoothly and excel rapidly as you move ahead in your AI journey.
