There’s a growing demand for qualified AI engineers. The rate companies are hiring indicates the large scale of the AI market. According to Indeed, employers’ demand for AI talents has more than doubled over the past three years and the number of AI job postings has increased by 119%!
Tech giants such as Google, Microsoft, Apple and Amazon are pouring in billions of dollars into AI products, services and talent. With the right guidance and skills, one can master this hot field.
Become an AI engineer by mastering these 5 skills
1. Get a bachelor’s degree in a relevant field
The first step to becoming an AI engineer is having a basic graduate-level education qualification in a relevant field such as Mathematics, Statistics, Computer Science, Information Technology, Economics or Finance. A bachelor’s degree is enough for a student to start their journey in an entry-level position. However, if he/she wants to be in the competitive AI job market, a master’s degree in Computer Science with a specialization in AI or ML courses is crucial. One can also aim to achieve PhD certification in specialized areas including Machine Learning, Deep Learning, Computer Vision and Natural Language Processing.
2. Brush up your programming and statistical skills
Programming, statistics, and math knowledge are other essential AI engineer skills. Start by learning programming languages such as:
It’s imperative to have a thorough understanding of programming concepts such as:
- Data structures
- Static methods
- Recursion and loops
- OOPS (Object-Oriented Programming System)
- Software development life cycle and
Quick tip: Start by practicing Python as most machine learning engineers use this language because of its easy-to-learn syntax, versatility and large library.
For statistical and applied maths skills, one needs to be familiar with calculus, matrices, derivatives, and understand models such as Naive Bayes, Gaussian Mixture and Hidden Markov Models. Engineers must have in-depth knowledge of algorithm theory and how they work. Successful ML engineers should have experience solving Gradient Descent, Lagrange, Quadratic Programming, Partial Differential equations, and so on.
3. Learn basic AI frameworks
Most of the time, AI engineers focus on learning how to build and deploy AI models. But the most important skill to master is to have a complete understanding of AI/ML workflow which includes data preparation, AI modeling, simulation and testing and deployment. Without robust and accurate data to train with, an AI model is likely to fail. For this, one should learn processes such as data classification, clustering, regression, human insights, data cleansing, and discretion.
The next step is AI modeling where one must learn how to create a robust, accurate model that can make decisions based on data. Engineers should start building the concepts of deep learning (neural networks), and machine learning (decision trees). The third step is simulation and accuracy testing where engineers validate that a model is working properly. For validation, engineers should learn tools such as Simulink. The final step is deployment. Engineers need to master making an implementation-ready model that can be fitted into a designated hardware environment.
4. Experiment with data sets and tools such as TensorFlow and Keras
Now that you know the fundamentals of AI tools, programming languages, frameworks, it’s time to get hands-on and involved in mini-projects. Learn where you can find free public data sets that can be anything from hospitals, criminology department to Wikipedia and Google trend data sets. Get to know Kaggle Datasets, where most AI engineers hunt for publicly available datasets and even refer to other projects built with the same data set. Following this, engineers should also understand powerful reinforcement learning tools such as TensorFlow and APIs such as Keras.
5. Learn business skills
In order to pitch the AI models and products to stakeholders, AI engineers need to think like business leaders and communicate well. That is why they are required to learn business skills such as critical thinking, problem-solving, leadership, project management, industry knowledge, and networking. Engineers need to explain AI and ML concepts to people with less knowledge about AI and should learn good communication skills, and solve problems quickly.
Some interesting AI jobs Facts and Statistics
- According to Gartner, AI is set to create 2.3 million jobs by the end of 2020, leading a net gain of 500,000 potentially new jobs.
- According to International Data Corporation (IDC), the number of AI jobs is expected to globally grow 16 percent this year.
- IDC forecasts by 2024, the AI market is expected to break the $500 billion mark with a five-year compound annual growth rate (CAGR) of 17.5% and total revenues reaching an impressive $554.3 billion.
- According to Indeed, the Average Salary of an Artificial Intelligence Engineer is around $110,000 per Annum, with a minimum of $105,244 and a maximum of $144,611.
- Multinational companies such as Amazon, Google, Microsoft, NVIDIA, Facebook, Intel, Rocket Fuel, General Electric, Cylance, Oculus VR, Booz Allen Hamilton, Huawei, Adobe, Accenture, iRobot, Magic Leap, Rethink Robotics, BAE Systems, HERE, IBM, Samsung, Lenovo, MoTek Technologies, Uber, PCO innovation, Rakuten Marketing, and Wells Fargo are the leading employers who are hiring top AI engineers since 2018.
Engineers should consider taking certification courses to master the above skills. To be a successful AI engineer, completing a certification course in Data Science, Machine Learning or Artificial Intelligence is highly recommended.
Fusemachines envisions democratizing AI and has been educating AI, targeting students at the K-12 level all the way through the graduate level, especially in underserved communities. To provide integrated AI courses, Fusemachines has partnered with multiple schools and colleges in Nepal, Latin America and other South Asian countries. The company has opened Fuse.ai center, which is an AI research and training center offering blended AI courses through its proprietary fuse.ai platform. The proprietary curriculum includes courses in Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision. Certifications like this will help engineers become leading AI industry experts and envision a fulfilling and ever-growing career ahead.