Artificial Intelligence is creating a significant impact in our daily lives. We are using AI knowingly or unknowingly. for instance, a simple WhatsApp chat or Google Search has an AI algorithm in it that helps you to research more on the search topics. It has ingrained in our daily activities. Likewise, most companies are eager to adopt AI technology in their businesses, marketing, and sales models, or creating a software solution.
This has invariably led to an increase in the demand for AI professionals. Let’s talk about becoming an Artificial Intelligence Engineer here. They earn about USD 1,15, 320 in the United States as per salry.com reports. In brief, AI is a game-changing technology, and professionals with AI engineering knowledge are the game-changers.
Read on further to know how to become an AI engineer.
Becoming an AI engineer in 2021
Studies say that about 133 million new AI jobs will be created by 2022. There are plenty of opportunities for AI professionals, especially AI engineers offered by the industrial growth revolution. So, it is the best-recommended choice for you for this decade.
As an AI engineer, you will be:
- Building, testing, and deploying new AI models
- Coordinating with data scientists to collect and analyze business data
- Discussing with business analysts to understand the business objectives and problems
- Automating infrastructure, and converting ML models into APIs
- Using machine learning throughout the software development cycle
- Training ML models to conduct domain-specific tasks
- Developing MVPs and improving algorithms
- Applying the right AI technologies at all levels
So, now as you are clear with the job profile, let’s understand the technical skills you need to ace your interview as an AI engineer.
In general, an AI engineer career demands experience in Mathematics and Science-related topics. If you are a graduate/Master’s in Computer Science, Information Technology, Finance, Economics, or Mathematics, it’s highly appreciated. In addition, you should be well-versed in technical skills such as programming languages, Statistics, Applied Mathematics, Algorithms, Frameworks, Natural Language Processing, Deep Learning, Neural networks, and big data technologies. let’s get a little deeper into these skills here.
- Learn Python, R, Java, and C++.
- Data structures and classes.
- Be familiar with memory management, basic algorithms, and links.
They come in handy while building and implementing AI models.
- Linear algebra, statistics, and probability.
- Matrices, vectors, matrix multiplication, standard deviations, means, and Gaussian distributions. Naïve Bayes, Gaussian Mixture Models, and Hidden Markov Models.
Knowledge in these domains helps you practically implement solutions in your organization.
Applied Mathematics and Algorithms
- Learn Lagrange, Gradient Descent, and Partial differential equations
- Quadratic programming
- Deep learning algorithms
This knowledge helps you in building AI models using unstructured data.
Big data technologies
- Cassandra, and
These technologies help you derive information from data.
Natural language processing
- Sentimental Analysis
Deep learning and neural networks
- Speech recognition
- Image classification
Be a problem solver. In addition to theoretical knowledge, you should be able to use the knowledge to solve real-world problems. Solve the existing business problems, derive benefits, and come up with new solutions. To be a successful AI engineer, you should be a problem solver.
So, be a problem solver to become a successful AI engineer. Also, validate your learning skills by earning the best AI engineer certification available in the market. Learn about one AI certification – AIETM, which is more popular in the industry here.
Get introduced to AIETM Certification
Earning an AI certification is a practical way to master the subject. AI certification helps you land a job easily as compared to your non-certified peers.
Artificial Intelligence Engineer – AIETM is one of the best-known AI certifications in the industry, provided by the Artificial Intelligence Council of America. The course work broadly covers the domain such as machine learning, supervised/unsupervised learning, natural language processing, and more.
Learn AI and be a part of the AI revolution by getting certified today!