Top 10 AI Jobs that will be On-Demand in the Industry in 2022 – Analytics Insight



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November 22, 2021

AI

By the end of the decade, the rapid-paced advances in AI are likely to progress into our daily lives. AI-powered machines and software will eventually detach themselves from human supervision, embarking on their journey as sentient beings. Currently, artificial intelligence is impacting every industry around the globe. AI’s growth rate has allowed its market to capture brilliant sources of revenue globally since it is now possible to understand the need of the customers. The need to leverage these advanced technologies is crucial to businesses, which has accelerated the demand for skilled AI professionals. Many AI jobs have gained popularity this year due to recent tech innovations. So, in this article, we have listed the AI jobs that will gain more popularity in 2022.

Artificial Intelligence Specialist: AI specialists apply their skills in engineering and computer science to create machines and software programs. Some AI specialists also work in cognitive simulations, in which computers are used to test hypotheses about how the human mind works. The key contribution of an AI specialist is to use emerging technologies, such as ML, neuro-linguistic programming, and other technologies to solve business problems in new and creative ways.

AI Engineer: AI engineers are responsible to build AI models using machine learning algorithms and deep learning neural networks to draw business insights. These insights are used to make crucial business decisions that can affect the entire organization and its reputation. To become an AI engineer, the candidates must possess a deep understanding of programming languages, software development, and data science. Also, having a bachelor’s degree in computer science, engineering, or in other IT domains would act as a bonus.

AI Research Scientist: Aspiring research scientists should have multiple degrees in domains like computational statistics, applied mathematics, and machine learning. They will be a crucial part of the entire development process of a product or a prototype. Some of their primary responsibilities also include planning and conducting experiments, writing research papers and reports, and demonstrating various procedures.

Data Engineer: Data engineers work in a variety of settings to build systems that collect, manage, and convert raw data into usable information for data engineers, scientists, and business analysts. Their ultimate goal is to make data accessible so that organizations can use it to evaluate and optimize their performance.

Machine Learning Engineer: ML engineers are not only involved with customer insights and risk management but are also a crucial part of additional initiatives, which continuously simplify ML principles from a business perspective. They should also possess data management skills to handle the vast amounts of business data and insights. This job role specifically attracts candidates who are inclined towards neural networks or cloud applications.

Business Intelligence Developer: BI is a huge part of artificial intelligence because the candidates are responsible for optimizing a variety of business processes with their analytical and BI-centric capabilities. The developers use data analytics and technology to share valuable business information with the decision-makers of the company.

AIOps Engineer: AIOps engineers develop and deploy ML algorithms that analyze IT data and boost the efficiency of IT operations. Moderate and large businesses dedicate several human resources for real-time performance monitoring and anomaly detection. AI software engineering allows business leaders to automate their processes and optimize labor costs. The candidates aspiring for this job role should know areas like networking, cloud technologies, and security.

Cloud Architect for ML: Cloud architects are responsible for managing the cloud architecture in an organization. This profession is gaining more momentum as cloud technologies are becoming more complex. Cloud architects should possess skills with configuration management tools like Chef, Puppet, and Ansible. They will also need to learn coding languages like Go and Python.

Computational Linguist: Computational linguists take part in the creation of ML algorithms and programs that are used for developing online dictionaries, translating systems, virtual assistants, and robots. Computational linguists have similar responsibilities to that of ML engineers. The only difference is that computational linguists combine their deep knowledge of linguistics with computer systems to approach NLP.

AI Systems Designer/Researcher: Human-centred AI systems designers make sure that intelligent software is created by keeping the end-user in mind. The AI system designer is a research-heavy position so the candidates need to possess a Ph.D. degree in human-computer interaction, human-robot interaction, or any other related domains.

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