Caltech Bootcamp / Blog / /

Exploring the Artificial Intelligence Career Path

Artificial intelligence career path

Artificial intelligence (AI) is a hot subject today. It is everywhere, filling people with either enthusiasm or dread. However one feels about AI, there is undeniably plenty of career potential.

This article explores the artificial intelligence career path and discusses the skills and educational requirements needed to enter the field. We’ll also explain why AI is a good career choice, the best AI career paths, how to get started with AI and machine learning training, and how to get promoted.

So, why should you be studying artificial intelligence, anyway?

Why Study Artificial Intelligence?

Here are the primary reasons why you should study artificial intelligence today.

  • Artificial intelligence benefits society. Despite some critics’ alarm at the rise of artificial intelligence, the field offers many benefits to humanity. You can use your AI skills to help drive innovation and provide aid in helping solve society’s problems. AI has produced significant advances in healthcare, manufacturing, and customer service.
  • Artificial intelligence-related jobs are increasing. The more advanced that AI becomes, the more versatile it gets. Initially, AI was limited to space exploration and computing. Now, it can be found in banking, cybersecurity, healthcare, manufacturing, finance, automotive, and many more industries.
  • The AI job market is skyrocketing. Many companies are integrating AI into their work, especially in automation. The more companies adopt AI into their everyday workloads, the more demand there will be for people trained in the field.

Also Read: Machine Learning in Healthcare: Applications, Use Cases, and Careers

Is Getting into Artificial Intelligence a Good Career Move?

As seen in the previous section, AI is a hot commodity and is even considered “future proof,” meaning that AI-related jobs won’t become a casualty of continued progress but will be around for many years.

The U.S. Bureau of Labor and Statistics shows that employment in computer and technology is projected to grow faster than average from 2022-2032, with over 377,000 openings available each year.

Besides, as we will see later, learning about AI can serve as a launch point for many IT-related occupations.

Although these things sound promising, they do beg the question: What skills should you master for such a career?

What Skills Are Required for an AI Career?

Here are some skills you should master, to one degree or another, if you want to get on an AI career path.

Hard (Technical) Skills

  • Artificial intelligence platforms. Google Cloud AI, IBM Watson, Microsoft Azure AI.
  • Big data tech. Such as Hadoop or Spark.
  • Database management. Apache Cassandra, Couchbase, DynamoDB, etc.
  • Data analysis and statistics. MATLAB, Pandas, R.
  • Data acquisition systems. For example, physical and wireless sensors.
  • Data manipulation tools. For example, Pandas, SQL, NumPy.
  • Digital marketing goals and strategies
  • General knowledge in the IT industry
  • General-purpose languages. For example, Python, R, Java, or C/C++. Multiple languages are an ideal approach.
  • Experience in an AI sub-field. Like computer vision or natural language processing.

Soft Skills

  • Adaptability
  • Analytical skills
  • Communication (verbal, written, etc.)
  • Detail-oriented
  • Effective collaboration
  • Management and leadership
  • Problem-solving

How does someone acquire these skills? Soft skills are only sometimes taught. More often, people practice and improve. However, technical skills are learned through formal education, typically a combination of higher education and continuing education, such as online courses, bootcamps, and certificate programs. Our next section will elaborate.

Also Read: What is Machine Learning? A Comprehensive Guide for Beginners

Educational Requirements for AI Careers

Remember, artificial intelligence isn’t a single career; it’s an umbrella under which many different careers can be found. Thus, there isn’t one generic AI educational curriculum; the specifics will vary according to your desired job.

Generally, most AI positions require a bachelor’s degree in computer science, engineering, mathematics, physics, or a related technical field. Many organizations want candidates with advanced degrees (e.g., Master’s, Ph.D.), which are preferred for more complex, specialized roles.

In some instances, a certification can substitute for some education requirements. It depends on the organization, the position being applied for, and the experience level required. It would be easier to get an entry-level position using certification than it would for a senior position.

Now, it’s time to look at some of the best artificial intelligence career paths.

The Top Artificial Intelligence Career Paths

First, here’s a generic, non-specific career path template to follow:

  • Undergraduate study. Obtain a bachelor’s degree in computer science, data science, or related field.
  • Grad school. Earn a master’s degree or PhD in AI or a related field.
  • Entry-level. Get a job as an entry-level data scientist or AI engineer.
  • Mid-level. Advance to a more senior position as an AI engineer or a data scientist.
  • Senior-level. Assume a leadership position as a data scientist or research scientist.
  • Expert. Become a recognized expert in the field of artificial intelligence. Conduct your research and publish your findings in top academic journals.

You can tweak the above template to your specific AI-related career. The following are the best career paths you can take in AI.

  1. Big Data/Data Engineer. Big Data engineers collect data, transform it, and offer accessibility and quality control. Data engineers design, develop, and maintain data pipelines for AI and ML apps.

o Salary: $128,490 annual average.

o Path: A degree in computer science or related field. Learn Big Data technologies like HD and SRK. Get involved in real-world data projects.

o Potential job advancements: Big Data engineer, data architect, data scientist, etc.

  1. Machine Learning Engineer. Machine learning experts are one of the most found jobs after artificial intelligence. These experts build and instruct the teaching of Artificial Intelligence (AI) systems via machine learning and work with algorithms, data preparation, and model evaluations.

o Salary: $166,001 annual average.

o Path: Acquire a solid foundation in math, ML theory, and Python programming, supplemented by a healthy amount of ‘hands-on’ experience through projects.

o Potential job advancements: Machine learning engineer, research scientist, AI developer.

  1. AI Research Engineer. AI researchers focus on expanding knowledge and applying Artificial Intelligence.

o Salary: $145,000 annual average.

o Path: Earn a PhD in AI or AI-related studies, get immersed in academic research, and stay updated with new AI developments.

o Potential job advancements: Research scientist, lead personnel.

  1. AI Developer. AI developers develop applications, solutions, and systems based on AI algorithms. They mostly write code and develop the software used in AI programs.

o Salary: $103,590 annual average.

o Path: A degree in computer science or an adjacent field, proficiency with AI development tools and languages, and ability to create an AI project portfolio.

o Potential job advancements: App developer, software engineer, AI application developer.

  1. Data Scientist. Data scientists employ machine learning and statistical analysis to pull knowledge from data. They help solve complex problems, build predictive models, and make data-driven decisions.

o Salary: $121,500 annual average.

o Path: Study data science, statistics, or related subjects, learn programming languages, and acquire experience in data analysis and visualization.

o Potential job advancements: Data scientist, data analyst, business analyst.

  1. Robotics Engineer. Robotics engineers are involved in robot hardware and software operations.

o Salary: $115,663 annual average.

o Path: Study robotics, computer science, or related subjects, gain experience with robotics projects, and learn programming.

o Potential job advancements: Robotics engineer, automation engineer, robotic systems developer.

  1. Computer Vision Engineer. These engineers design computer-based intelligence systems that translate and understand visual information, typically used in image and video analysis, object recognition, and driverless cars.

o Salary: $127,792 annual average.

o Path: Study computer vision, algorithms, machine learning, and programming languages. Participate in computer vision projects.

o Potential job advancements: Image processing specialist, visual intelligence developer.

  1. Business Intelligence Analyst. Business Intelligence analysts rely on data to make organizational decisions, designing reports, dashboards, and data visualization instruments to help organizations make intelligent choices.

o Salary: $91,063 annual average.

o Path: Acquire a degree in business, data analysis, or another relevant field. Become acquainted with current data visualization tools. Participate in real-world business intelligence projects.

o Potential job advancements: BI analysts, data analysts, analytics managers.

Also Read: Machine Learning Interview Questions & Answers

How Do AI Professionals Get Promoted?

There’s no big secret to getting promoted in your AI field. Follow these pointers, and your odds of getting that coveted senior position will improve.

  • Master advanced artificial intelligence concepts. Acquire a more profound knowledge of complex AI ideas such as deep learning, natural language processing, and reinforcement learning. You will stand out if you develop competence in these areas.
  • Never stop learning. The field of artificial intelligence is always growing and evolving. Thus, you must keep abreast of new ideas and best practices via online training, attending conferences, and reading publications such as journals and research papers. If you are committed to learning, you become an asset to the company.
  • Specialize. Once you’ve mastered the basics of your position to your satisfaction, see if you can find a narrow focus in the AI field that matches your interests and abilities. Specializing in any of AI’s many subfields can be a way to ensure that you become an indispensable asset to the company and the industry as a whole!
  • Learn how to collaborate and communicate better. Collaboration and communication are vital elements of effective communication and teamwork. Put ego aside and always strive to be a team player while looking for ways to be an excellent communicator.
  • Cultivate leadership skills. Senior positions typically require some leadership roles. So, develop leadership qualities by demonstrating initiative, volunteering for extra projects and tasks, and offering to help lead project teams.

How Do You Start Your Career in AI?

Acquiring an entry-level position is the best way to start on your AI career path. Fortunately, the requirements for many AI entry-level jobs are more relaxed than those for advanced positions. If you have met your educational requirements and have at least a beginning grasp of the hard and soft skills, you’re already well on your way.

If you’re already working in an organization and want to transition into an AI career, consider volunteering to help with AI-related projects. Find a mentor who can help guide you on your AI career path. Check out online AI forums and begin forging connections and acquaintances. Also, online AI and machine learning courses are a great way to develop the latest skills.

Do You Want to Get Better Acquainted with AI and Machine Learning?

To develop those valuable career skills, check out this post graduate program in AI and ML. This online bootcamp teaches Python, ML, NLP, and more using high-engagement virtual classes and practical projects. Get your certificate and make your AI career path a reality.

You might also like to read:

How to Become an AI Architect: A Beginner’s Guide

How to Become a Robotics Engineer? A Comprehensive Guide

Machine Learning Engineer Job Description – A Beginner’s Guide

How To Start a Career in AI and Machine Learning

Career Guide: How to Become an AI Engineer

Artificial Intelligence & Machine Learning Bootcamp

Leave a Comment

Your email address will not be published.

AI Deepfakes

The Double-Edged Sword of AI Deepfakes: Implications and Innovations

Explore the world of deepfake AI in our comprehensive blog, which covers the creation, uses, detection methods, and industry efforts to combat this dual-use technology. Learn about the pivotal role of AI professionals in ensuring the positive application of deepfakes and safeguarding digital media integrity.

What is a ROC Curve

Performance Modeling: What is an ROC Curve?

Explore the ROC curve, a crucial tool in machine learning for evaluating model performance. Learn about its significance, how to analyze components like AUC, sensitivity, and specificity, and its application in binary and multi-class models.

Artificial Intelligence & Machine Learning Bootcamp


6 months

Learning Format

Online Bootcamp

Program Benefits