How to become an Artificial Intelligence Engineer – A Roadmap

The demand for a skilled, professional artificial intelligence engineer is at an all-time high, thanks to the rapid development of Artificial Intelligence and Machine Learning in several aspects of the IT sector.

Annually, tech behemoths such as Google, Microsoft, Apple, and Amazon invest billions of dollars in AI products and services. According to reports, the global AI sector is growing at a staggering rate of 154 percent.

Many industries have begun to embrace artificial intelligence (AI) approaches in order to obtain better outcomes and profitability, and developing AI-powered products is a promising activity.

The demand for someone with AI abilities has already doubled in the last few years, resulting in a surge in job growth.
The following are some of the top companies that hire AI developers in India:

• NVidia
• Google
• Accenture
• Amazon
• Uber
• Facebook

As a result, in this tutorial, we shall walk you through the steps of becoming an Artificial Intelligence Engineer.

What is Artificial Intelligence?

Artificial Intelligence (AI) is the ability of a computer system to imitate human behavior. Machines exhibit this form of intelligence, which can be compared to the natural intelligence expressed by people and animals.

Machines that use artificial intelligence (AI) learn from previous data and behaviors, whether positive or bad. With this fresh information, the system can fix itself to avoid recurrence of the errors, as well as make any necessary adaptations to handle new inputs. Finally, the computer is capable of doing tasks that are comparable to those performed by people.

In the following segment, we’ll look at what an AI engineer performs.

What exactly is an AI Engineer?

We must first determine what responsibilities you would play if you pursued a career in AI:

1. Researcher in Machine Learning

This work entails looking at the theoretical side of AI. This function entails assisting in the improvement of innovation or its application to new areas. They are typically taught by Masters or Ph.D. holders.

2. Program Manager

These designers use artificial intelligence to analyze a set of data. This position necessitates strong programming and numerical skills.

3. Information Analysis and Data Mining Engineers

Modeling and developing deep learning frameworks for seeing and responding to designs.

4. AI Engineers

An artificial intelligence engineer uses algorithms, neural networks, and other technologies to make progress in the field of artificial intelligence. These experts could work on a variety of artificial intelligence projects in a variety of industries, for example.

• Healthcare
• Retail

Engineers can also pick between weak and powerful artificial intelligence projects, with distinct setups focusing on different capabilities. There are a variety of ways to get there now. So, let’s have a look at how to work as an Artificial Intelligence Engineer.

What Does an AI Engineer Do?

An AI engineer creates AI models that use machine learning algorithms and deep learning neural networks to extract business insights that may be used to make large-scale business decisions. Depending on the aims they intend to achieve, these engineers develop weak or strong AIs.

Programming, software engineering, and data science are all skills that AI engineers possess. They use a variety of tools and strategies to process data and build and manage AI systems.`
The following section of How to Become an AI Engineer discusses an AI engineer’s responsibilities.

Artificial Intelligence Engineer Job Responsibilities

AI Engineers in general, depending on their level of expertise:

• Prototypes in Data Science are being studied and transformed.
• Appropriate machine learning and artificial intelligence techniques and technologies should be researched and implemented.
• Create Machine Learning Applications that Meet the Needs
• Working with the Robotics Team and Electric Engineers
• Choose relevant Datasets and Representation Methods for your data.
• Experiment with Machine Learning and Artificial Intelligence.
• When Necessary, train and retrain systems
• Follow the most recent advances in the field.

How long does it take to train as an AI engineer?

The demand for AI workers is anticipated to continue to rise, according to the World Economic Forum. Developing applicable abilities can assist you in becoming a qualified AI engineer. Getting the necessary education, abilities, and professional experience, on the other hand, can take a long time.

Gaining admittance into this sector, according to Springboard, an online course provider usually necessitates a foundational understanding of:

• Computer programming
• Cognitive science
• Robotics
• Math
• Engineering
• Physics

One caveat to this lengthy educational path, according to Springboard, is that if you’re already a software engineer, you might be able to jumpstart your career in artificial intelligence by taking a few AI-focused classes at a traditional institution or online school.

How to Become an AI Engineer: A Step-by-Step Guide

You’ll need an analytical thought process and the capacity to solve problems with cost-effective and efficient solutions to succeed in Artificial Intelligence careers. AI developers must also be aware of technological advancements that result in cutting-edge software. The next sections will provide you with an overview of the skills you’ll need to land the AI job of your dreams.

1. Obtain An Educational Credential

Let’s begin with the fundamentals. You must first have a bachelor’s degree. To become an AI engineer, you’ll need a degree in one of the following subjects: computer science, mathematics, information technology, statistics, finance, or economics. Solve challenges with low-cost, high-efficiency solutions. AI developers must also be aware of technological advancements that result in cutting-edge software.

2. Improve Your Technical Abilities

If you want to learn how to become an AI engineer, you’ll need to improve your programming skills as well as your software development processes and practices. Make sure you’re well-versed in the following topics, both theoretically and practically:

• Programming languages
• Statistical knowledge
• Applied Maths and Algorithms
• Natural Language Processing
• Deep Learning & neural networks
• Spark
• Big Data Technologies

Let’s look at the technical skills that are required in further detail.

3. Programming Languages

You’ll need a solid grasp of programming languages such as Python, C++, R, Java, and others. A solid understanding of classes and data structures is essential. Knowing how to use these tools isn’t always enough. You might be working on a project that requires you to apply your hardware expertise to improve something. You should be comfortable with basic algorithms, classes, memory management, and linking.

4. Statistical Knowledge

When it comes to statistical expertise, you’ll need to understand matrices, vectors, and matrix multiplication. You should be fine if you know how to work with derivatives and integrals. Statistics will be discussed frequently. At the absolute least, be sure you grasp Gaussian distributions, means, and standard deviations. You’ll also need a solid grasp of probability to comprehend models like:

• Guileless Bayes
• Gaussian Mixture Models
• Shrouded Markov Models

5. Algorithms and Applied Mathematics

You must have a thorough understanding of algorithm theory and how algorithms function in order to be a great AI engineer. Gradient Descent, Lagrange, Quadratic Programming, Partial Differential Equation, and other courses will be required in Artificial Intelligence. If you’ve been away from math for a time, it could appear scary at first. Be aware that Machine Learning and Artificial Intelligence require far more math than, say, front-end programming.

6. Natural Language Processing

Linguistics and Computer Science are two important fields of Machine Learning and Artificial Intelligence that are combined in Natural Language Processing. It’s quite likely that you’ll be able to deal with text, audio, or video. As a result, having good control over libraries like Gensim and NLTK, as well as algorithms like word2vec, Sentimental Analysis, and Summarization is critical.

7. Neural Networks and Deep Learning

Machine Learning may be required for activities that are too difficult for humans to code directly. Neural networks have a role in this. The human brain, which can discern numerical patterns based on sensory data, is fashioned after neural networks.

Single-layer neural networks have evolved naturally in the Artificial Intelligence sector to Deep Learning neural networks, in which data is transmitted through multiple layers for more complicated pattern recognition. Deep neural networks have proven to be the most accurate method of handling complicated issues such as translation, speech recognition, and image classification, all of which are crucial in AI.

8. Apache Spark

Spark is an analytics engine that is primarily used for large-scale data processing. It includes high-level APIs in Scala, Java, Python, and R, as well as an updated motor that allows general calculation diagrams for data analysis.

It also supports a number of higher-level devices, such as Spark SQL for SQL and DataFrames, MLlib for AI, GraphX for chart preparation, and Structured Streaming for stream processing.

9. Big Data Technologies

Enormous Data Technology can be defined as a software utility for analyzing, processing, and extracting data from large datasets. This is something that traditional data processing software could never handle. We need Big Data Processing Technologies to analyze this massive amount of real-time opportunity data and come up with conclusions and predictions to mitigate future risks.

Hadoop, Pesto, MongoDB, and other Big Data technologies are among the most important.

Business Skills for Artificial Intelligence Engineer

You’ll need some business talents in addition to your technical skills to land an artificial intelligence career. Some of the business skills needed to be a successful AI engineer include:

• Thinking outside the box
• Effective communication
• Analytic problem-solving skills
• Industry knowledge

1. Thinking Outside The Box

It is critical for an AI engineer to have creative reasoning abilities. Developing AI is all about thinking outside the box and being creative, which necessitates the use of novel reasoning.

2. Ability To Solve Problems

AI is a means for humans to solve complex issues. Problem-solving abilities are essential for the development of such technology.

3. Effective Communication

The importance of teamwork cannot be overstated. Teamwork is usually preferred in the technology field as well.

4. Industry Knowledge

To work in any industry, it is necessary to have a broad understanding of the industry and its connected fields. AI engineers are in the same boat.

Skills Used by Experts in Artificial Intelligence

Artificial intelligence is a field that is based on science. The attempt to use robots to emulate the human brain necessitates specific understanding. As a result, the abilities that artificial intelligence experts use on a daily basis will differ based on the type of project you’re working on. Consider the following AI engineer jobs and their associated skills:

Consider the following AI engineer jobs and their associated skills:

1. AI academic researcher: To come up with novel or unique techniques to test machines’ real-world viability, an artificial intelligence academic researcher may need strong hypothesizing skills.

2. AI algorithm developer: This individual may be in charge of converting AI research into mathematical models that can be implemented using hardware and software. This may necessitate a high level of inventiveness and problem-solving abilities.

3. Computer scientists and software developers: These individuals are frequently charged with writing software that analyses, interprets, and makes judgments. They might employ analytical skills and attention to detail on a daily basis.

A typical day in the life of an AI expert does not have a set template. The talents you’ll need will differ depending on your position.

Salary and Career in Artificial Intelligence

Talented persons with the necessary AI education may be in great demand for AI employment, according to supply and demand principles. Though the Bureau of Labor Statistics (BLS) does not specifically report on artificial intelligence jobs, it does give data on computer and information technology jobs in general.

According to the BLS, employment of computer and IT occupations is predicted to rise at a pace of 12% from 2018 to 2028, which is significantly faster than the overall growth rate of 5% reported by the BLS. This equates to approximately 546,200 new jobs. Cloud computing, big data, and information security developments, according to the BLS, will boost this demand. These professionals earned an average of $88,240 in 2019.

Here are some possible AI careers, according to the BLS list of computer and IT occupations:

• Computer and information research scientists External link: $122,840
• Computer network architects External link: $112,690
• Software developers External link: $107,510
• Information security analysts External link: $99,730
• Database administrators External link: $93,750

If you’re ready to start a career in AI engineering, look through our list of AI jobs to discover one that fits your talents and interests.

Conclusion

According to Indeed, an Artificial Intelligence Engineer’s average annual salary is roughly $110,000, with a minimum of $105,244 and a high of $144,611.

Startups like Argo AI and industry giants like IBM are among the companies that hire elite AI personnel. These are the top employers who employed top AI talent in the last year, according to Glassdoor.

To become an AI engineer, you must work hard and be patient. Practice is essential for learning AI, just as it is for acquiring any other ability. The more you train yourself, the better you’ll get at it.