In today’s world, where technology keeps getting more and more sophisticated with each passing day, the demand for Artificial Intelligence (AI) jobs is increasing at an exponential rate. A report by World Economic Forum (WEF) clarifies that AI will generate 133 million new jobs by 2022. As professionals in the tech industry, you have many job titles to explore when it comes to AI, machine learning (ML), or any other IT specialization. Let’s look at the details of an AI engineer job description, including the skills, education, and experience needed to stay at the top of this growing field!
AI Engineer Job Description
An AI engineer is an IT specialist who plans and implements special algorithms to assist enterprises in learning, analyzing, and making future predictions.
A typical AI engineer job description includes the following points:
- Ability to create AI models utilizing deep learning, neural networks, and ML algorithms to derive business insights and fuel informed decision-making
- Well versed in programming, software engineering, and data science fields
- Ability to utilize various tools and methods to process data as well as create and keep up AI systems
- Knowledge to build a variety of AI applications, including language translation, visual recognition or perception, and targeted advertising based on sentiment analysis
AI Engineer Skill Set
An aspiring AI engineer needs to have a clear understanding of the skills required to thrive in this field. These skills typically include both technical skills and soft skills.
Programming is the first qualification needed to become an AI engineer. Learning computer languages like Python, R, Java, and C++ to design and implement models is essential to become well-versed in AI.
- Statistics, Probability, and Linear Algebra Knowledge
An AI engineer needs to have a thorough understanding of linear algebra, probability, and statistics to comprehend and use various AI models, such as hidden Markov models, Naive Bayes, Gaussian mixture models, and linear discriminant analysis.
- Big Data and Spark Technology Expertise
AI engineers work with huge amounts of data, including terabyte- or petabyte-scale production-level data that is streaming in real time. To be able to comprehend and handle such large data sets, engineers need to be familiar with Apache Spark and other big data technologies. In addition to Apache Spark, one can also employ Hadoop, Cassandra, and MongoDB, which are all big data technologies.
- Knowledge of Popular Algorithms and Frameworks
Building machine learning models becomes simple with hands-on experience on techniques such as linear regression, KNN, Naive Bayes, and Support Vector Machine. Additionally, AI engineers should be able to comprehend deep learning algorithms (such as a convolutional neural network, recurrent neural network, and generative adversarial network) and implement them using a framework to develop AI models using unstructured data. Theano, TensorFlow, Caffe, and PyTorch are a few examples of such artificial intelligence frameworks.
The difference between a successful AI engineer and those who struggle to excel in this field lies in their soft skills. Although an AI engineer is fundamentally a technical role, the ability to effectively communicate with others, deal with problems, manage time effectively, and work in collaboration with others are the driving factors that determine a project’s success. A few of the soft skills that AI engineers must possess include: effective communication, problem-solving abilities, time management, team work, and critical thinking.
AI Engineer Educational Qualifications
Although the discipline of AI engineering is still relatively new, it has the potential to lead to a wide range of career possibilities in the years to come. For the entry-level AI engineer, a bachelor’s degree in a relevant subject, such as information technology, computer engineering, statistics, or data science is a minimum requirement. Following a Bachelor’s Degree in Computer Science, you can pursue a Master’s in Artificial Intelligence. Certifications in data science, deep learning, and ML will make it easier for aspirants to find employment and demonstrate the mastery of key concepts. You can explore online courses on AI and ML on Emeritus to gain an edge.
AI Engineer Experience Requirements
The roles and responsibilities of an AI engineer vary with the level of seniority. The following are the AI engineer experience requirements, depending on the role:
- Junior AI Engineer
To apply for this role, candidates need to possess two-plus years of relevant industry experience. The responsibilities of a junior engineer include:
- Assessing and contrasting algorithm performance using sizable, actual-world data sets
- Analyzing data from several sources to draw conclusions
- Creating and putting ML algorithms into use
- Conducting research to develop scalable technology solutions for problems encountered in the real world
- Establishing large-scale data analysis, scalable model validation, and model implementation
- Senior AI Engineer
Senior AI engineer aspirants need to possess over five years of relevant industry experience. The responsibilities of a senior engineer include:
- Directly collaborating with the front end and the back end
- Training new and retraining existing AI networks
- Conducting research and development of new AI models
- Concept creation and quality assurance of the entire AI system
- Promoting organizational vision to support the business
- Managing and executing projects
AI Engineer Salary Range
According to Glassdoor, the national average compensation for an AI engineer is $120,385 in the U.S. Moreover, top companies like IBM offer a median yearly remuneration of $158,748. Bank of America pays between $67 and $73 per hour, while Google pays about $116,358 average salary per year.
The variation in salary is based on the education, industry, location, organization, and the knowledge and experience candidates bring to the table.
Can an AI Engineer Work Remotely (Work from Home)?
AI engineers generally spend the majority of their time programming or writing code. Hence, their position is ideal for remote work. However, sometimes employers may hire them to work on-site partially, so this depends on the specifics of the job scope. Overall, AI engineers can work remotely or from home.
AI Engineer Certifications
A certification proves your proficiency in the field and your ability to adhere to industry standards. Consider pursuing a certification in addition to formal education via online courses to advance in an AI engineer role. We have listed a few examples of general AI engineer certifications below:
- AI Google Education
- Microsoft Certified: Azure AI Engineer Associate
- AI Engineer Certification: Artificial Intelligence Board of America
Upskill with Emeritus
Given the AI engineer job description, you must have realized how important upskilling is in this career. Since both AI and ML are rapidly expanding fields, doing so will help you stay updated with the latest industry developments to land a high-paying AI job. Courses from Emeritus are perfect for this endeavor. Explore our wide range of online AI and ML courses and give your career the necessary boost!
By Rupam Deb
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