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Top 8 Artificial Intelligence Career Paths for 2024

23rd Dec, 2023
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    Top 8 Artificial Intelligence Career Paths for 2024

    In the fast-changing technological environment, AI has come up with a new dimension of changes in the industry and the way people communicate. The demand for professional, proficient individuals capable of applying AI applications toward innovation, solutions to complex issues, and making informed decisions is rising as artificial intelligence applications are diffusing in various sectors. 

    According to Hostinger, AI is going to create more than 133 million job opportunities by the year 2030. So, this is the reason why the Artificial Intelligence career path appears to be very attractive and exciting, pulling people coming from different academic backgrounds onto a course that looks as if it can reconstruct the future. In this blog, we will explore the career paths of Artificial intelligence and what skills are most important to consider while taking this journey.

    AI as a Career Choice

    The development of Artificial Intelligence (AI) offers a promising career option for those interested in understanding how technology can assist with data and problem resolution. As an AI professional, you’ll focus on developing intelligent algorithms and systems that take up tasks that could only be performed by humans.

    There are various career options in artificial intelligence that you can consider if you want to be a machine learning engineer, data scientist, AI researcher or an AI ethicist. Additionally, AI professionals are enjoying an increased demand due to the increased integration of AI into different sectors. 

    Combining engaging intellectual work, multiple job opportunities, and having the chance to influence humanity positively make up an attractive career path for people who are into technology and innovation. So, now is the right time to enrol in AI classes online if you’re thinking of starting your career in AI.

    Career Paths in AI

    Professionals have diverse options in the field of artificial intelligence. In this regard, we will describe some of the typical AI engineer career paths, like positions, tasks, pay rates, qualifications needed, and jobs under every type, as shown below. However, keep in mind that salaries and job titles can differ due to location, level of expertise, and how much you have held on to the prerequisites to learn artificial intelligence.

    1. Big Data/Data Engineer

    • Roles & Responsibilities: Big Data Engineers accumulate data, transform it, and provide accessibility as well as quality control. Data engineers design and maintain data pipelines to make data available for AI and ML apps.
    • Salary: The amount differs depending on your previous experiences and where you live. It is common for entry positions to have initial salaries that range between $60,000 and $90,000, although it will grow as one gains more experience.
    • How to become: Get a degree in computer science or any other related field, master big data technologies such as HD and SRK, and be involved in real-world data projects.
    • Job Titles That Follow: Positions like Big Data Engineer, Data Architect, Data Scientist etc.

    2. Machine Learning

    • Roles & Responsibilities: Machine learning expert is one of the most common jobs after artificial intelligence, where experts construct and instruct the learning of Artificial Intelligence (AI) systems through machine learning. Algorithms, data preparation and model evaluations.
    • Salary: The average Machine Learning Engineer’s salary ranges between $ 90,000 – $ 150,000 or higher, based on experience and area.
    • How to become: Get a good grounding in maths, ML theory, and Python programming, with a healthy amount of ‘hands-on’ experience via projects.
    • Job Titles That Follow: Machine learning engineer, research scientist or AI developer.

    3. AI Research

    • Roles & Responsibilities: The next career path for artificial intelligence is to go for AI research, where researchers centre on expanding knowledge as well as the application of Artificial Intelligence. Their experiments, journal articles, and further enhancements of AI are some of their efforts.
    • Salary: These salaries differ, but an AI researcher who has worked for a while may make over $150,000.
    • How to become: To pursue a PhD in AI or AI-related studies, get involved in academic research, and be up to date with current AI developments.
    • Job Titles That Follow: Research Scientist, Lead Personnel.

    4. AI Development

    • Roles & Responsibilities: AI Developers develop applications, systems, and solutions based on AI algorithms. Their operations mainly revolve around writing code and developing software used in AI programs.
    • Salary: For this job opportunity, salaries are between $80000-$13000k, based on experience and location.
    • How to become: The career path for an AI engineer requires a degree in computer science or a similar field and becoming fluent with AI development tools and languages while creating an AI project portfolio.
    • Job Titles That Follow: App developer, software engineer, and AI application developer.

    5. Data Science

    • Roles & Responsibilities: Through statistical analysis as well as machine learning, Data Scientists derive knowledge from data. Such roles help in solving complex problems, building predictive models, and making data-driven decisions.
    • Salary: Senior data scientists can earn more than $150000, while entry-level positions pay between $7000 to $100000.
    • How to become: Study data science, statistics, or related areas, learn programming languages and gain expertise in data analysis and visualisation. You can also take Data Science certification courses to get your skills validated and earn a standpoint for your resume.
    • Job Titles That Follow: Data Scientist, Data Analyst and Business Analyst.

    6. Robotics 

    • Roles & Responsibilities: Another significant occupation of the artificial intelligence career guide is robotics engineering. In some cases, they are involved in both hardware and software operations with respect to robots.
    • Salary: There are also differences in Robotics Engineer salaries, ranging between $70000 – $100000 as starting figures.
    • How to become: Study robotics, computer science or related areas, have experience with robotics projects and learn programming.
    • Job Titles That Follow: Robotics Engineer, Automation Engineer, and Robotic Systems Developer.

    7. Computer Vision

    • Roles & Responsibilities: Next, computer vision is a great career path in artificial intelligence, where you would design computer-based intelligence systems that translate and comprehend visual information. They can be used in image and video analysis, object recognition and a car that doesn’t need drivers.
    • Salary: Depending on the level of expertise and locality, salaries generally lie between the range of $80,000- $130,000 or even higher.
    • How to become: Study computer vision algorithms machine learning, select programming languages and take part in some computer vision projects.
    • Job Titles That Follow: An engineer in computer vision, image processing specialist and visual intelligence developer.

    8. Business Intelligence

    • Roles & Responsibilities: Business Intelligence analysts rely on data in making organisational decisions. They design reports, dashboards and data visualisation instruments to help organisations make intelligent choices.
    • Salary: Salaries may be very diverse but usually begin around $60,000 – $90,000. 
    • How to become: You also need to have a degree in business, data analysis, or any other relevant field, get acquainted with popular data visualisation tools, and participate in real-world business intelligence projects.
    • Job Titles That Follow: BI analysts, data analysts and analytics manager.

    How Do AI Professionals Get Promoted? 

    AI careers have planned paths as they involve strategic approaches towards professional development. Below, I have discussed the road to artificial intelligence and how you can get promoted for further career growth and advancement in professional and organisational ladders.

    A. How Long Does It Take to Get Promoted to Senior Level?

    The career scope in artificial intelligence is on the rise, but one needs to be patient to ensure constant growth in this field. The time taken to promote the AI professionals to the senior level depends on the organisation’s structures, experience and skills of the professional and the dynamics of the AI industry.

    However, in most cases, it may take between three to five years for an AI specialist to attain the senior level. However, most experienced and senior AI experts must possess knowledge in this area and be able to work with sophisticated AI schemes. In addition, they should have the capacity to guide and train other artificial intelligence associates.

    B. Tips and Tricks 

    Achieving an Artificial Intelligence promotion to a senior level is more than just knowing your job; it involves having a successful career strategy. Here are some valuable tips and tricks to help you progress in your artificial intelligence career prospects:

    • Master Advanced AI Concepts: Gain a more profound knowledge about complex AI ideas such as deep learning, reinforcement learning and natural language processing. Developing competence in such areas makes you unique among other people.
    • Continuous Learning: There are constant changes in the AI field. Keep abreast of new ideas and best practices through online training, conferences, and publications like journals and research papers. By showing that you are committed to learning, you can become a good asset to the company.
    • Specialise: Determine where you can find a narrow focus in the field of artificial intelligence suited to your interests and abilities. Specialising in any of the subfields could be one way of ensuring that you become an indispensable asset not only to your company but also to the industry at large.
    • Collaborate and Communicate: These are essential elements of effective communication and teamwork which will help you on how to get into Artificial Intelligence career. Interact with fellow students in teams and describe what you know to your classmates. You should always be a team player and be an excellent communicator in order to make things move faster for you.
    • Leadership Skills: You must develop your leadership qualities despite being in a technical capacity. Demonstrate initiatives, volunteer for extra tasks, and run projects. 

    Skills Required in Artificial Intelligence

    A. Soft Skills

    Soft skills are as important in the world of Artificial intelligence as technological expertise. 

    • Communication: It is always essential for AI professionals to master communication skills because they must communicate intricate concepts with different non-technical and professional individuals. 
    • Problem-solving: Another important soft skill is problem-solving because, in AI, it’s always a matter of finding new solutions to complicated issues. 
    • Adaptability: Adaptability is important since AI is always changing, and one should be willing to learn all the time. 
    • Ethical reasoning: Lastly, ethical reasoning is an important component in ensuring that AI is developed responsively as well as making decisions with responsibility attached to it.

    B. Hard Skills

    It is important to note that hard skills are the technical skills that form the foundation of an AI career. The basis of all this is mathematics and statistics, which provide a solid base for AI and help you with how to get into AI field. 

    Since many times AI development necessitates coding, it is important to be fluent in programming languages such as Python, Java, and C++. Also, data science is a must, including gathering, cleaning up and analysing data. 

    Furthermore, it is important to have NLP skills because they are required for working with text and language data. Finally, a computer vision expert is important to support image movie applications.

    C. Additional Skills

    Additionally, AI practitioners gain extra skills, such as soft and hard skills. This means that domain knowledge is very important because most AI applications are specific to certain industries. Therefore, it is vital to understand the context in which AI application operates. The best way to learn these skills is to enrol in KnowledgeHut AI classes online and get the best online training to learn in the best way. 

    Demand for big data skills, including working with huge volumes of data, is also very high. This includes knowledge of Cloud computing in general, and especially AWS, Azure, or Google Cloud, to ensure the scalability of AI’s solutions. 

    How to Kickstart an AI Career?

    When starting an AI career, first of all, make sure you have a good educational background in mathematics, programming (Python), machine learning, and deep learning. Try applying your knowledge by being involved in hands-on tasks such as Kaggle tournaments and establishing one portfolio. Be different by choosing a niche AI domain, such as computer vision or NLP.

    Follow new trends in AI by reading research articles and blog posts. Attend industry conferences and be involved in relevant online networks. Seek appropriate certifications, pursue internship programs or seek entry-level jobs within this industry, which will bring in the necessary experience.

    With ongoing learning, AI is a developing field. As you move along in your career path, be calm and determined while working on improving your qualities. To become a successful machine learning engineer, data scientist, or AI researcher, one needs constant learning and experience.

    Wrapping Up

    Finally, choosing a career in Artificial Intelligence (AI) is not an easy walk in the park. It has got more options as well as obstacles that you must overcome on your journey. More and more companies need AI experts, which makes prospects look optimistic for those who are going to pick this way out. Be it the first step for any student venturing into the AI domain, a professional looking forward to a job change, or just a curious learner who wants to discover what more AI can do, the future looks bright.

    Frequently Asked Questions (FAQs)

    1Is AI a good career path?

    Absolutely, the job market holds strong opportunities for AI with rising demands, stimulating intelligence and significant societal influence.

    2Do I need Python for AI?

    Yes, I would strongly recommend Python because it is a simple and rich AI libraries and frameworks ecosystem.

    3What skills and background do I need to pursue a career in artificial intelligence?

    For starters, you must be good in mathematics, programming skills (Python being the most crucial one), as well as machine learning and data analysis. However, a person could have a background in computer science and associated fields without being an exception.

    4What is the outlook for a career in AI?

    The outlook for artificial intelligence looks great, with increasing possibilities across different sectors, emphasising ethics in AI, and creating bright and vibrant times for AI practitioners.


    Ashish Gulati

    Data Science Expert

    Ashish is a techology consultant with 13+ years of experience and specializes in Data Science, the Python ecosystem and Django, DevOps and automation. He specializes in the design and delivery of key, impactful programs.

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