Five Major Technical Skills Required to Become an AI Engineer in 2021

Technologies like AI and machine learning are proved as powerful tools to aid business functions. In return, offers effective solutions to solve complex business problems and can act independently in certain situations even software engineers or aspiring AI engineers should not foresee.

From using smart assistants like Alexa and Siri to music and media streaming apps like Netflix or Spotify, AI is here to make our lives easier. While people are worried about automation and AI replacing humans, a McKinsey report demonstrated 27 percent of business growth in AI investments. The number of job opportunities across the world continues to increase, more so, there are now five times larger than it used to be in 2013.

As a result, specialists in AI are in constant demand across industries related to face recognition software, robotics, gaming, speech recognition, expert system, search engines, vision recognition, weaponry, and more. Therefore, if you’re planning to launch a career in this field, probably now is the right time for a career transition.

AI skills worth learning in 2021

AI is not a fad, it will keep evolving till the foreseeable future. After all, building machine learning models or mimicking the human brain will require more than just theory. The skills required to pursue an AI career vary from one job role to another job role, however, all of them require common technical skills and focus.

Below are the common technical skills required:

Programming Languages (Python, R, and Java): You need to have in-depth knowledge in programming languages, most preferably Python, R, and Java. As an aspiring AI engineer, a critical understanding of data structures is a must-have. Python is easy to learn and is also referred to as the lingua franca of machine learning.
Mathematics, Linear Algebra, and Statistics: Gaining extensive familiarity with concepts related to vectors, matrices, matrix multiplication is mandatory. How else do you expect to understand and implement different AI models like Naïve Bayes, linear discriminant analysis, and Hidden Markov Models?

To gain success in the AI field, you need to have in-depth knowledge about how algorithms work and the theory behind them. Additional knowledge in subjects like Quadratic Programming, Gradient Descent, Partial Differential equation, and Lagrange is an additional advantage.
Neural Network Techniques: When tasks get too complex for humans to understand or code, machine learning might come in handy. This is where neural network techniques step in – helps recognize patterns according to the sensory data. Precisely, neural networks have been the most used approach used to solve problems related to speech recognition, translation, and image classification.
Natural Language Processing (NLP): NLP combines two major areas in AI and machine learning – computer science and linguistic. There’s a possibility of you likely to work with text, audio, and video, therefore, it is necessary for you to have great command over libraries like NLTK, and Gensim, and other related techniques such as Summarization, word2vec, and Sentimental Analysis.
Big Data Technologies: Data is one area wherein AI engineers will extensively be working with – streaming data in petabytes or terabytes. For them to work with large volumes of data, they need to have a great understanding of big data technologies such as Spark, Hadoop, MongoDB, and Cassandra.

An AI engineer could earn an average salary of USD 114,121 per annum in the U.S., according to Glassdoor. However, salary may differ according to the type of organization, the type of industry, location, and experience in the field.

Getting skilled in AI can provide multiple opportunities in job roles such as AI Developer, AI Architect, Data Scientist, Machine Learning Engineer, and Business Intelligence Developer.

As industries invest in AI and machine learning, becoming an AI engineer is perhaps an ideal choice you’re making today.