The Most In-Demand Popular AI Jobs

Artificial intelligence (AI) has taken the world by storm. Diverse industries, including health, technology, and education, have incorporated it into their systems to enhance their operations, competitiveness, and customer experiences.

As AI integration deepens, the demand for skilled AI professionals also increases. LinkedIn data shows a notable trend: job listings featuring AI or generative AI have doubled from July 2021 to July 2023, highlighting AI’s growing significance in the job market.

This article explains ten hot AI jobs that you must consider pursuing because of their high demand and a range of other benefits.

Data scientist

Expected average salary: $156,352 per year, according to Glassdoor

Data scientists extract digital data from different sources, such as databases and social media. They refine raw data to organise it and make it more understandable. Once done, they employ numerous techniques, including clustering and regression, to extract useful insights from this data.

They make data-driven future predictions, find hidden patterns, and resolve intricate problems.

Furthermore, they play an essential role in sharing their findings with other teams, contributing to the organisation’s informed decision-making processes and enhancing overall performance.

Data scientists possess a wide variety of skills, including programming, machine learning, statistical analysis, and data manipulation.

Software engineer

Expected average salary: $139,558 per year, according to Glassdoor

In the AI field, software engineers are responsible for designing, developing, testing, and maintaining software that facilitates AI applications.

First, they write codes to develop software according to the project objectives. Next, they design software systems and perform extensive testing to make the system error-free. They ensure the software is according to the user’s needs.

Once the software is deployed, they maintain it to keep it functional and secure throughout its lifespan. Software engineers also document their systems to help others understand their technical specifications.

Must-have skills for software engineers include programming, backend development, web development, software architecture, testing and debugging.

Data engineer

Expected average salary: $124,965 per year, according to Glassdoor

Data engineers play multiple roles in an organisation. Their key responsibility is to design, develop, and install data pipelines. In addition, they construct secure and well-maintained data storage systems.

Their expertise extends to creating Extract, Transform, Load (ETL) systems, ensuring the safe extraction of required data, its transformation for analytical purposes, and its seamless integration into storage systems.

Data engineers ensure data scientists and analysts always have access to processed data so they can use them whenever they want to draw beneficial insights.

If you want to pursue this career, you must learn programming languages, data modelling, database design, data integration, and data security-related skills.

AI research scientist

Expected average salary: $132,286 per year, according to Glassdoor

As the name indicates, AI research scientists conduct research in the AI field to develop and implement new AI technologies and applications.

They spend a lot of time reading publications to collect valuable data, stay abreast of dynamic shifts within the AI world, and draw meaningful conclusions. They develop prototypes, conduct experiments to test their AI-related hypothesis and develop algorithms.

Essential skills for AI research scientists include programming skills, mathematics, statistics, research methodology, experimentation, and machine learning.

Product manager

Expected average salary: $156,417 per year, according to Glassdoor

Product managers handle all the important stages of an AI-driven product’s or system’s lifecycle. They help define product strategy according to the customers’ needs, the organisations’ goals and the development team’s capabilities.

Once a product is launched in the market, they gather user feedback and may update products according to the changing user demands. They identify and remove risks associated with the product development, performance and success.

Product managers play leadership roles in an organisation. They collaborate with different teams to ensure all are on the same page. Additionally, they possess strong problem-solving and communication skills.

Machine learning engineer

Expected average salary: $151,527 per year, according to Glassdoor

Machine learning engineers (ML engineers) work in the AI field to design, build, and maintain programs and systems that can retrieve data and employ it in various tasks.

Their workflow begins with collecting and refining data sets to ensure their compatibility with machine-learning models. Next, they fine-tune these models by carefully selecting the most appropriate algorithms.

They assess the model’s performance, tweak its parameters and conduct rigorous testing to ensure it operates correctly. ML engineers integrate machine learning models into systems and regularly monitor them to address issues on time.

An ML engineer must be proficient in programming languages and have in-depth knowledge of machine learning algorithms and libraries.

Robotics engineer

Expected average salary: $117,370 per year, according to Glassdoor

Robotics engineers perform a wide range of tasks to create robots and robotic systems from scratch. They develop all the necessary elements of a robot, including its mechanical, electrical and software components.

They also write codes to control their functions and behaviour. They incorporate sensory systems into them to enable them to interact with their surroundings. They ensure that the built robot can navigate through different environments and avoid obstacles.

Robotics engineers use their AI knowledge to make robots extract data from different sources and apply it to solve specific challenges. They further engage in rigorous experimentation to assess the performance of robotic systems and refine algorithms as needed.

If you aspire to pursue this career path, ensure you have enough knowledge of mechanical engineering, electrical engineering, programming and robot operating systems (ROS).

NLP engineer

Expected average salary: $152,578 per year, according to Glassdoor

Natural language processing engineers create systems that understand and generate natural language data or human language.

Their core responsibilities involve crafting algorithms that excel in a range of language-related tasks. This includes functions such as text summarization, language translation, and text classification based on various factors, including domain specificity.

NLP engineers are expected to possess a versatile skill set that shows their technical expertise, domain knowledge and problem-solving abilities. They work in different fields, including tech companies, AI research, and customer service.

AI quality assurance manager

Expected average salary: $117,156 per year, according to Glassdoor

AI quality assurance (QA) managers play a crucial role in evaluating AI systems, ensuring they not only meet the company’s quality objectives but also comply with ethical principles.

Their responsibilities begin with the creation of quality assurance systems aligned with industry standards. They then proceed to assess AI system performance through a variety of tests. They document their findings and share them with stakeholders and other teams to get their valuable input.

Companies seek AI quality assurance managers with a diverse skill set that includes programming, scripting, software testing, machine learning, and critical thinking.

Key factors driving AI job demand

The following factors contribute to the increased demand for AI professionals.

Advancement in technologies: Technology is evolving rapidly, which is creating new possibilities for people to integrate AI into systems and improve their overall capabilities and performance.

Data explosion: The relentless growth in internet usage results in a staggering production of 328.77 million terabytes of data per day. Industries require proficient tools to extract valuable insights from this vast data. It increases the need for AI professionals who can create, integrate, and maintain such tools.

Industry adoption: Diverse sectors, including healthcare, technology, education, and finance, are increasingly leveraging AI to gain a competitive edge. Consequently, they are seeking a larger pool of AI talent to manage tasks related to AI tools.

Innovation and research: Users’ demands evolve with time. Industries need experts to test and explore different algorithms and technologies in order to generate superior AI systems that meet users’ needs.

Funding and investment: Governments and companies are channelling more resources into the field of AI, further fueling the demand for skilled individuals in this rapidly expanding industry.

What should you do to become an AI professional?

These steps will guide you in pursuing a career in the AI field.

  1. First things first, get an AI-related degree that shows your expertise in the field and your ability to contribute to a company’s success. A bachelor’s degree in a relevant field, such as computer science or data science, is a minimum requirement.
  2. Next, seek out training and gain practical experience. It will allow you to test your skills and improve them over time.
  3. Once you get enough knowledge and learn the required skills, focus on building an impressive resume and portfolio.
  4. Participate in relevant conferences and workshops to connect with like-minded individuals in the industry.
  5. To find AI job opportunities, utilise search engines like Google and job posting websites such as Indeed, Glassdoor, and LinkedIn, all of which are free and effective tools.
  6. Prepare thoroughly for job interviews, ensuring that you arrive on time and present yourself professionally.
  7. Remember that the AI field is constantly evolving. Stay updated by continually acquiring new knowledge and skills.

Summing it up

AI professionals are in high demand across different industries. Hence, now is the ideal time to become a part of this field. Get a relevant degree, gain hands-on experience, and apply for your dream job.

Join the world of AI to bring revolution to industries, shape the future, and contribute to ground-breaking innovations.

How to Become a Data Scientist Without a Degree?

With 328.77 million terabytes of data being created every day, the role of a data scientist has now become increasingly popular worldwide.

This surge in data creation has sparked interest among many people, including those without a formal degree. However, there is often confusion about whether it is possible to join this field without a degree and, if so, how one can actually achieve it.

If you, too, have this confusion, you have come to the right place. In this article, we will explore the reality of becoming a data scientist without a traditional degree. So, let’s get started!

Can you become a data scientist without a degree?

Traditionally, a degree, particularly in fields like computer science or mathematics, has been required for data science roles. However, things have changed now.

Due to the availability of extensive online resources, it has now become easier than ever to become a data scientist without a formal education.

Also, companies now value practical skills and real-world experience more than mere academic degrees that do not guarantee you have the skill set required for that particular role.

While a degree can be beneficial for credibility, it is not an absolute necessity for a career in data science.

An 8-step guide to becoming a data scientist without a degree

1.      Start with self-learning

The journey to becoming a data scientist without a formal degree begins with self-learning. Self-learning offers numerous benefits, including flexibility and diversity.

Platforms like Coursera, Kaggle, DataCamp and edX provide courses designed by top universities and industry leaders. These courses often include hands-on projects, providing practical experience that is highly valued in the industry.

Many YouTube channels complement these courses by offering free, high-quality tutorials that make complex concepts accessible. Moreover, books and academic journals can also provide in-depth understanding.

Self-learning can be hard for some people as they do not have a mentor or environment that pushes them to work hard. To make this journey successful, remember that the key is to maintain a structured approach.

2.      Learn key concepts and skills

To excel in data science without a degree, it is essential to gain an understanding of key concepts and skills. This involves concepts from various disciplines, such as statistics, mathematics, programming, machine learning, and data analytics.

Statistics form the backbone of data science as it allows you to understand and apply various analytical techniques.

Similarly, programming is crucial as these are the primary tools used in data analysis and machine learning. Python, SQL, R, and VBA are the top programming languages that a data scientist must know.

Additionally, you should have a strong grasp of machine learning concepts to understand complex data patterns.

3.      Gain proficiency in data science tools

After learning the basic skills and gaining enough knowledge, it is time to learn data science tools. These tools are required for transforming raw data into meaningful insights.

Start by mastering Python libraries like TensorFlow, Keras, Pandas, and NumPy. They are required for data manipulation and analysis.

Familiarize yourself with data visualization tools like Power BI, Datawrapper, and Infogram. They will help present complex data findings in an understandable and visually appealing manner.

Learn version control systems, like Git, to track and manage code changes while collaborating with others.

You should also get expertise in big data technologies such as Hadoop, Spark, and Apache Kafka. They will all help you analyze large-scale data sets and extract value from them.

4.      Work on your soft skills

In data science, soft skills are as important as technical skills are. These skills allow you to communicate your findings, collaborate with teams, and make better data-driven decisions.

Develop strong verbal and written communication skills. They will allow you to explain complex data insights more simply to non-tech individuals in your team.

Work on your problem-solving skills as well. Data scientists often face challenging scenarios where critical thinking and quick decision-making are required. Having these skills will help them navigate through complex data challenges effectively.

Time management and project management skills are also essential. These help prioritise tasks, meet deadlines, and smartly manage resources and projects.

Enhancing all these soft skills not only improves your professional effectiveness but also significantly increases your value as a data scientist in any organization.

5.      Gain hands-on experience

Gaining practical experience is invaluable in the journey to becoming a proficient data scientist without a degree. It allows you to apply theoretical knowledge to real-world scenarios, sharpening your skills and understanding.

Start with personal projects or data challenges on platforms like Kaggle. It offers a multitude of problems to solve that not only help build your technical skills but also showcase your problem-solving capabilities.

Internships or part-time roles in data science can also provide practical experience. These opportunities often offer mentorship from experienced professionals. Even roles like data analysis or business intelligence that are somewhat related to data science can provide relevant experience.

These experiences are not just about technical skills. They help develop soft skills like problem-solving, critical thinking, and creativity.

6.      Create an online presence

Showcase your skills, experiences, and interests to a global audience by creating an online presence.

Start by identifying your professional goals and the audience you want to reach so you know what platforms to use and which kind of content to create.

Next, create profiles on relevant platforms. LinkedIn is the preferred choice for professionals. It is where you can show your experience, share industry-related content, and connect with peers and leaders in your field. Platforms like X (Twitter) and YouTube can also be equally beneficial.

Since the competition is high on these platforms, you must maintain consistency by regularly updating your profiles. Share recent accomplishments, projects, or articles you have written. Boost your visibility by engaging with others and contributing to discussions.

Turn your online presence into an evolving portfolio that attracts interested hiring companies and helps them see your worth.

7.      Connect with professionals

Networking is essential for building any career, especially if you do not have a formal degree. It allows you to connect with industry experts and learn from them. Additionally, it opens doors to new opportunities and insights.

Platforms like LinkedIn,, and dedicated data science forums are excellent for connecting with peers. Participating in discussions, webinars, and Q&A sessions on these platforms can enhance your visibility.

You should also try attending industry conferences, seminars, and workshops. They will allow you to learn about the latest trends and technologies in data science.

Volunteering for data science projects can also be a great way to network and gain experience at the same time. It can showcase your commitment to the field and lead to meaningful connections.

8.      Stay updated with industry trends

The field of data science is continually evolving, which is why a commitment to lifelong learning is essential.

You can achieve this goal by reading industry blogs, attending webinars, enrolling in advanced courses, and participating in forums. Learning about all the updates and trends ensures that your skills remain relevant and competitive in the job market.

Entry-level data scientist jobs

Entry-level data scientist roles provide an opportunity to enter into this evolving field. These roles are typically assigned to recent graduates or individuals transitioning from related fields.

Some examples of these roles include:

  • Data coordinator
  • Business analyst
  • Data collector
  • Machine learning engineer (entry-level)
  • Data science consultant (junior level)
  • Quantitative analyst
  • Statistical analyst
  • Research analyst
  • Data engineer (entry-level)
  • Operations analyst
  • Marketing analyst
  • Business intelligence analyst
  • Junior data scientist

What does a data scientist do?

A data scientist’s role is multifaceted. He/she has numerous responsibilities in an organization, including:

  • Data collection and processing: Gathers raw data from various sources and cleanses it for analysis.
  • Statistical analysis of the data: Applies statistical methods to interpret data.
  • Machine learning and predictive modelling: Develops models using machine learning techniques.
  • Data visualization: Creates graphs, charts, and other visual representations of data to help others understand data findings.
  • Problem-solving and strategy development: Uses data-driven insights to solve business problems and help build better strategies.
  • Collaboration and communication: Works with other teams and communicates findings to stakeholders.
  • Continuous learning: Stays updated with the latest trends and adapts to new tools as required.

How much does a data scientist earn?

Data science is a high-paying field. However, the salary of a data scientist can vary widely depending on various factors such as location, industry, and experience.

On average, a data scientist can earn from $131000 to $189000 in the US. It includes the base and additional salaries (commissions, bonuses, etc.).

Conclusion: Let your data science journey begin

The journey to becoming a data scientist without a traditional degree is both challenging and rewarding. It requires dedication, consistency and determination.

Start this journey with self-learning. Gain knowledge through online resources and become proficient in essential data science tools. Sharpen your skills by doing personal projects. Build an online presence and connect with others.

Once you have enough experience and knowledge, apply for your dream job to start your career, where your findings can make a huge impact.

Tips for Getting a Job in AI: What You Need to Know

The global AI market is projected to reach a whopping $190.61 billion by 2025, indicating a wide variety of career opportunities in this sector. This increase in growth reflects technological advancements and signifies the growing acceptance of AI solutions across various industries.

If you are thinking about pursuing a career in this exciting and expanding field, now is the best time to jump in.

This article explains expert tips and tricks to help you get your dream AI job. So, let’s get started and take the first step toward a promising future in AI.

1.     Understand the basics of AI: Set the foundation

Learn the key concepts

Understanding the foundational principles is crucial when you want to land an AI job.

Algorithms form the backbone of the AI systems. These are set instructions that computers use to process information and execute tasks. Another important branch of AI is machine learning. It allows systems to learn from data and refine their decisions accordingly.

Get familiar with programming languages

A variety of programming languages are used to create and manage AI systems. Python, R, C++, Java, Lisp, and Prolog are a few languages you must be familiar with. Python is one of the most popular languages in this field due to its readability and simplicity.

Some AI professionals also use Java, C++, or even specialised languages like Julia. The key is to be versatile. The more languages you know, the more valuable you become in the job market.

Understand data management

Data is used to create and run AI engines. That is why you must know how to collect, clean, analyse, and store data.

Knowing how to handle different databases (organised data collections that can be easily accessed, managed, and updated) is also crucial. It will allow you to feed the right data into your algorithms, making them more effective and accurate.

2.     Educational requirements: What you need

Get a relevant degree

Having a relevant bachelor’s degree can be very advantageous for people interested in joining an AI career. Some people also go for a master’s or a PhD to get deep into the subject. According to Stack Overflow’s Developer Survey, about 24% of AI and machine learning space professionals have a master’s degree, and about 48% have a bachelor’s degree.

A degree in Computer Science will help you cover a broad range of topics that you will use in AI, such as algorithms, data structures, and programming.

Data Science degrees are helpful as they focus on statistical methods and data analysis techniques that are crucial in AI applications. Some people also opt for degrees in Mathematics, Engineering, or even Physics, as these fields offer relevant skills.

Do online courses

Online courses and certifications are effective in gaining specialisations. Platforms like Coursera, Udacity, and edX provide courses on specific AI topics, such as machine learning, neural networks, and natural language processing. These courses are often designed and taught by industry experts, offering real-world examples and hands-on projects.

Certifications from these courses can add value to your resume.

Publish research papers

Publishing research papers can help you stand out in job applications. Academic journals, conferences, and reputable blogs offer platforms to share your work and show your potential employers your talent.

Research also deepens your understanding of AI and shows your ability to solve complex problems and contribute to the AI field.

3.     Skill development: What employees are looking for

Learn coding

You should know how to code. Python, Java, and R are popular programming languages. Learning one or more of these languages will really help.

Get familiar with AI tools

Learn special tools that make AI work. These are called machine learning frameworks like TensorFlow and PyTorch. Learning these tools will make your job easier and impress employers.

Soft skills

Besides having technical skills, you should also have soft skills to work well with people. Problem-solving, communication, time management, critical thinking, adaptability, and teamwork are important soft skills employers look for in potential candidates.

In a LinkedIn survey, 92% of hiring managers said that soft skills are just as important or even more important than technical skills.

Gain hands-on experience

Hands-on practice is important in AI as it allows you to apply the knowledge gained in real-world problems, helping you understand challenges you will not find in textbooks.

Start with personal projects that solve specific problems. Internships and part-time jobs in AI-related roles can also provide practical experience. Some online platforms like Kaggle offer challenges that allow users to test their skills.

4.     Build a portfolio: Show off your skills

Use your portfolio as a tool to show your potential employers your skills. Show them what you can do for their company.

Make sure your portfolio is attention-grabbing. Create personal projects to show that you can identify a problem, come up with a solution, and complete it within time.

You should include a variety of work to indicate you have multiple skills. For instance, you could show how you used machine learning to predict stock prices or how you built a chatbot to remove the communication gap between a company and its potential customers.

5.     Network and mentorship: Make friends to get ahead

Go to events

Going to events like AI meetups, webinars, and conferences can help a lot. You will meet people who are also interested in AI; some might be looking to hire someone like you.

According to a study, around 85% of all jobs are filled through networking. So, make sure to show up and chat with people.

Join AI communities

AI communities offer opportunities for learning, collaboration, and even job placements. Online forums like Reddit’s Machine Learning community or specialised AI forums are also valuable resources for learning and networking.

Social media platforms like LinkedIn and Twitter are useful for following industry leaders, joining AI groups, and participating in relevant discussions.

Find a mentor

AI professionals face various challenges daily. You can deal with them successfully if you have someone to guide you. Mentors can give you advice, help you solve problems, and even introduce you to other important people.

A survey by Olivet Nazarene University found that 76% of people think mentors are important, but only 37% actually have one. So, finding someone experienced in AI who can help guide you is a good idea.

6.     Look for the AI job: Begin the hunt

Apply to job positions that match your skill level. You can look for them on online job forums like Glassdoor or submit your resume to AI companies so they can contact you when a vacancy becomes available.

Update your resume to showcase relevant skills and experiences in areas such as machine learning, data analysis, and programming languages.

7.     Prepare for the interview: Get ready to shine

If you have received an interview call, it is time to prepare for it to make a good first impression.

Practice answering common questions like “How do you handle large datasets?” out loud so you are ready. Review your coding and problem-solving skills as you might get a coding test or have to explain how you would tackle a machine-learning problem.

On the interview day, dress smartly, arrive on time, and listen carefully to the interviewer. Doing all these things can really help you ace that AI job interview!

8.     Keep learning: Stay updated

The AI field is always changing, so you must keep learning. Keep an eye on what is new in the AI world. Read industry news and academic papers to stay informed. Online forums and social media groups are also great places to learn about AI trends.

Why AI? The perks and potential

The following points indicate potential benefits of joining this field.

High demand: The demand for AI specialists is increasing with time. According to a report by LinkedIn, AI specialist roles have grown by nearly 74% annually over the past four years. That is a significant increase compared to other job sectors.

Versatility: The AI field offers a variety of roles that match different skill sets and interests. You could be a data scientist who focuses on analysing large datasets. Or, you could become a machine learning engineer who creates algorithms.

Competitive salaries: AI jobs are some of the most high-paying jobs in the world. According to Glassdoor data, the average salary for a machine learning engineer in the US is around $140,207 annually. It is more than the national average for all jobs.

Summing it up

Build a strong educational background and master languages like Python. Make sure to showcase your technical and soft skills in your portfolio.

Networking plays a big role, too, so go to events and consider finding a mentor. When it comes to interviews, prepare well by practising common questions and revising technical skills.

AI is an ever-evolving field that is full of opportunities, just waiting for someone like you to grab them. With the right skills and a passion for learning, you can excel in this field and impact the future of technology in many ways.