Artificial intelligence careers are in high demand. Artificial intelligence (AI) has created new opportunities during the past few years. It is creating waves throughout industries, making things that were previously inconceivable, like space exploration and melanoma diagnosis, possible. As a result, AI careers have also steadily increased; according to LinkedIn, AI professionals are among the “jobs on the rise.”
Don’t be scared of AI jargon; we have created a detailed AI glossary for the most commonly used artificial intelligence terms and explain the basics of artificial intelligence as well as the risks and benefits of artificial intelligence. After data architect, cloud computing, data engineer jobs, and machine learning engineers it’s time for artificial intelligence careers in the Hot and on the Rise series.
Artificial intelligence career paths
Are you interested in artificial intelligence careers? Professionals with expertise in artificial intelligence are more needed than ever because it is a technology that is becoming more and more common. The good news is that there are many job options in the AI sector, so you can assume a variety of tasks and responsibilities depending on the position, your background, and your pursuits.
Nearly every sector has a need for skilled AI workers, including:
- Financial services
- Government and military
- National security
- IoT-enabled systems
Prospects of artificial intelligence
There are several artificial intelligence career options. The list that follows contains both AI-related employment and certain roles that collaborate closely with people in AI jobs:
|Career Path||Description||Median Annual Salary|
|Data analytics||Making predictions about the future by identifying important patterns in data by looking at the past.||$65,000|
|User experience||Work with the items to ensure that customers can utilize them readily and understand their purpose. Recognize how users interact with technology and how computer scientists might utilize this knowledge to create more sophisticated software.||$76,950|
|Natural language processing||ChatbotsVirtual assistants||$108,609|
|Researcher||AI and computer science research. Learning how to advance AI technologies.||$77,576; base-level AI research roles average $93,103|
|Research scientist||Expert in computational statistics, machine learning, and deep learning. Expected to possess a graduate degree in computer science or a similar discipline that is supported by experience.||$99,809|
|Software engineer||Create software on which AI tools can run.||$87,403|
|AI engineer||Create AI models from scratch and aid stakeholders and product managers in understanding outcomes.|
|Data mining and analysis||Finding anomalies, patterns, etc. within large data sets to predict outcomes.||$77,586|
|Machine Learning Engineer||Analyzing massive data sets for anomalies, patterns, etc. to make predictions.||$146,085|
|Data scientist||Gather, examine, and interpret data.||$115,573|
|Business intelligence (BI) developer||Determine business and market trends by analyzing large, complicated data sets.||$92,278|
|12. Big data engineer/architect||Create technologies that enable data collection and communication between enterprises.||$151,307|
Do you need a guide for these jobs? Don’t worry, we already covered you.
Artificial intelligence career guide
Despite being a young and specialized area, artificial intelligence occupations are diverse. There are many artificial intelligence career options, each requiring a unique set of qualifications.
Let’s examine some of the most wanted.
Long ago, a data analyst was someone who gathered, purified, processed, and examined data in order to draw conclusions. These were primarily routine, repetitive chores in the past. With the emergence of AI, most of the mundane work has been mechanized. The data analyst position has therefore been promoted to join the new class of AI occupations. Data analysts today compile data for machine learning models, then use the results to create insightful reports.
An AI data analyst, therefore, needs to be knowledgeable about more than simple spreadsheets. They must be knowledgeable about:
- To extract/process data, use SQL and other database languages.
- Python for analysis and cleaning.
- Dashboards for analytics and visualization software, such as Tableau, PowerBI, etc.
- Understanding the market and organizational context with business intelligence.
The typical compensation for a data analyst is $65,000. However, high-tech firms like Facebook, Google, and others pay over $100,000 for employment as data analysts.
Machine learning engineer
Software developers and data scientists come together to form the field of machine learning engineering. They use big data technologies and programming frameworks to develop data science models that are production-ready, scalable, and capable of handling terabytes of real-time data.
The ideal candidates for machine learning engineer positions have backgrounds in data science, applied research, and software engineering. Candidates for AI positions should have a solid background in mathematics, and familiarity with deep learning, neural networks, cloud applications, and Java, Python, and Scala programming. Understanding software development IDE tools like Eclipse and IntelliJ is also beneficial.
In the US, a machine learning engineer makes on average $1,31,000. Pay at companies like Apple, Facebook, Twitter, and similar ones is substantially higher, averaging between $170,000 and $200,000. Learn more about the pay for ML engineers here.
Machine learning vs artificial intelligence article explains the differences between them.
Engineers in artificial intelligence (AI) with a focus on the spoken and written human language are known as natural language processing (NLP) specialists. NLP technology is used by engineers who work on voice assistants, speech recognition, document processing, etc. Organizations require a specific degree in computational linguistics for the position of an NLP engineer. They might also be open to hiring candidates who have a background in computer science, math, or statistics.
An NLP engineer would require expertise in sentiment analysis, n-grams, modeling, general statistical analysis, computer capabilities, data structures, modeling, and sentiment analysis, among other things. It might be advantageous to have prior knowledge of Python, ElasticSearch, web development, etc.
An NLP engineer has an average salary of $78,000, but with expertise, they can make over $100,000.
For a variety of goals, data scientists gather data, examine it, and draw conclusions. To extract knowledge from data and find significant patterns, they employ a variety of technological tools, procedures, and algorithms. This could be as simple as seeing anomalies in time-series data or as complicated as generating predictions about the future and giving advice. The following are the main requirements for a data scientist:
- Advanced degree in mathematics, computer science, statistics, etc.
- Statistical analysis and unstructured data comprehension.
- Having knowledge of platforms like Hadoop and Amazon S3 for the cloud.
- Expertise in programming languages like Python, Perl, Scala, and SQL.
- Working familiarity with Hadoop, Spark, MapReduce, Pig, and Hive.
A data scientist makes $105,000 on average. A director of data science role can earn up to $200,000 with experience.
Business intelligence developer
To find trends, business intelligence (BI) developers analyze intricate internal and external data. For instance, in a business that provides financial services, this could be someone who keeps track of stock market statistics to aid in investment selection. This might be someone who keeps an eye on sales patterns for a product company to help with distribution planning.
Business intelligence developers don’t really produce the reports, in contrast to a data analysts. For business users to use dashboards, they are often in charge of designing, modeling, and maintaining complex data on highly accessible cloud-based data systems. A BI developer is expected to possess the following skills:
- Engineering, computer science, or a related subject bachelor’s degree.
- Having practical knowledge of SQL, data mining, and other related topics.
- Knowledge of BI tools like Tableau, Power BI, etc.
- Powerful technical and analytical abilities.
For AI applications, software engineers create software. For AI jobs, they combine development activities such as creating code, continuous integration, quality control, API administration, etc. They create and manage the software used by architects and data scientists. They remain knowledgeable about current developments in artificial intelligence technology.
Software engineering and artificial intelligence expertise are prerequisites for an AI software engineer. In addition to statistical and analytical abilities, they must have programming skills. A bachelor’s degree in computer science, engineering, physics, mathematics, or statistics is often required by employers. Certifications in AI or data science might also help you get hired as an AI software developer.
Software engineers make $108,000 on average. Depending on your sector, specialization, and experience, this can reach $150,000.
When industrial robots began to gain popularity in the 1950s, the robotics engineer was possibly one of the first professions in artificial intelligence. Robotics has come a long way from the manufacturing lines to teaching English. Robotic-assisted surgery is used in healthcare. Robotic humans are being created to serve as personal assistants. All of this and more is what a robotics engineer does.
AI-powered robots are created and maintained by robotics engineers. Organizations often require graduate degrees in engineering, computer science, or a related field for these positions. Robotics engineers may be required to have knowledge of CAD/CAM, 2D/3D vision systems, the Internet of Things (IoT), as well as machine learning and AI.
Robotics engineers typically make $87,000 per year, but with experience and specialization, they can earn up to $130,000.
Big data engineer/architect
Big data engineers and architects create ecosystems that enable efficient communication between multiple business verticals and technology. As big data engineers and architects are often entrusted with planning, creating, and developing big data environments on Hadoop and Spark systems, this profession may feel more complicated than that of a data scientist.
Professionals with a Ph.D. in mathematics, computer science, or similar subjects are preferred by the majority of employers. However, because this position is more practical than, say, a research scientist, practical experience is frequently viewed as a strong replacement for a lack of academic degrees. Programming knowledge in C++, Java, Python, or Scala is required of big data engineers. Additionally, they must have knowledge of data migration, data visualization, and mining.
With an average compensation of $151,300, big data engineers are among the highest-paid positions in artificial intelligence.
Artificial intelligence job requirements
The traits that enable the most successful AI professionals to excel and develop in their jobs are frequently shared by these individuals. Working with artificial intelligence demands the capacity to think analytically and to come up with economical, efficient solutions to challenges. It also calls for insight into technological advancements that result in cutting-edge software that keeps organizations competitive.
AI experts also require technical expertise to create, maintain, and fix software and hardware. Finally, in order to do their jobs effectively, AI experts need to learn how to convey highly technical knowledge to non-technical audiences. This necessitates effective teamwork skills and effective communication.
Artificial intelligence education requirements
The majority of artificial intelligence programs are built on foundational computer science and math knowledge. A bachelor’s degree is required for entry-level work, whereas master’s and doctoral degrees are usually required for jobs requiring supervision, leadership, or administrative responsibilities.
Typical curriculum includes research into:
- Numerous math topics, such as probability, statistics, algebra, calculus, logic, and algorithms are covered.
- Neural nets or Bayesian networking are two examples of graphic modeling.
- Engineering, robotics, and physics.
- Coding, programming languages, and computer science.
- Cognitive science theory.
Candidates can search for degree programs with particular AI majors or pursue an AI emphasis within other majors like computer science, engineering, health informatics, graphic design, information technology, or engineering.
Artificial intelligence career salary
Jobs in artificial intelligence are in extremely high demand, and many of them pay well into the six figures. The precise figures will vary on a variety of elements, including the particular work responsibilities, industry, experience, level of education, and location.
However, this is a common range: A research engineer will make about $92,938 annually, according to Indeed, while a machine learning engineer would make about $150,183.
An artificial intelligence programmer typically earns between $100,000 and $150,000 each year, claims Datamation. On the other side, AI engineers make an average salary of $171,715 with top earners making over $250,000.
High pay are a result of the requirement for higher degrees and a rare mix of abilities.
Is AI a good career?
With a 31.4 percent growth in opportunities for data scientists and mathematical scientists, who are essential to AI, by 2030, the field of artificial intelligence has a bright future for career advancement.
The IT revolution is centered on artificial intelligence, which is constantly improving. AI is the driving force behind computer vision, speech analysis, and natural language processing. AI has a significant impact on business and society and will do so for a very long time.
The abundance of career prospects in the AI field is therefore not surprising; in fact, there are so many of them that the industry currently faces a unique problem: there are too many open positions and not enough competent applicants. The good news is that it provides nearly assured (and well-paying) work for those who are qualified.
Is AI difficult to learn?
Is AI difficult to learn? Yes, it can be, and 93 percent of automation technologists themselves feel underprepared for impending problems in the field of smart machine technology.
Artificial intelligence implementation presents various difficulties for businesses. Lack of personnel skills ranks as the biggest problem among them, affecting 56 percent of the businesses. Since AI is inherently difficult, it seems sense that most businesses feel this way.
It can be challenging to learn because of reasons like:
- Extensive programming: Programming is essential for AI. To teach computers to make their own decisions, you must learn how to code.
- Data proficiency: For machines to become skilled at an activity, they require a lot of data to learn from. Especially if you’re just getting started, getting this can be challenging.
- Complexity: Understanding AI requires knowledge of many disciplines, including computer science, statistics, calculus, and more.
- Lack of adequate tools: The majority of artificial intelligence tools and procedures in use today were created for conventional software. Newcomers to the sector frequently have to invest time and money in creating new tools, which can be challenging and time-consuming.
These figures do not, however, imply that there are no entry-level positions available in the field of AI and ML. Such employment opportunities abound, and you may get ready for them.
How long does it take to learn AI?
Although there is no denying that artificial intelligence’s future is bright, many people wonder how to get started in the field and how long it would take to master it. There is no clear answer to this query. In actuality, a variety of things have a role. But if you want the truth, studies have shown that it takes 10,000 hours to become an expert at any craft. Therefore, you could say that this also applies to machine learning.
Advanced ideas like deep learning, reinforcement learning, and unsupervised machine learning could require more time to learn. The length of the curriculum also affects how long it will take you to learn the skill because the majority of people who study artificial intelligence complete a certification program or course.
Artificial intelligence has advanced and improved people’s quality of life ever since its invention in the 1950s and continues to do so now in a variety of industrial contexts. As a result, an artificial intelligence profession will be fulfilling and sustainable for people who possess the ability to convert digital informational snippets into meaningful human experiences.
The majority of the current technology occupations are not careers in AI. Since AI is a rapidly developing discipline, experts working in the field must continually update themselves and keep up with new developments. AI/ML experts must regularly follow the most recent research and comprehend new algorithms; it is no longer adequate to just acquire abilities.
AI-related work opportunities are growing across a range of industries and are exciting and well-paying.
Additionally, AI is the subject of intense social and governmental scrutiny. AI experts need to consider AI’s social, cultural, political, and economic effects in addition to its technical components.
If you are asking “is artificial intelligence better than human intelligence“, we already have the answer. From the precursors of artificial intelligence to today, it is evolving and opening new opportunities for humanity. Artificial intelligence in developing countries is a suitable example of it. But can this one-sided interest change one day?
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