Transitioning Into AI Careers

The AI job market is expanding. Those who want to transition out of their current careers to work in AI require technical skills such as programming, machine learning, and a strong mathematical foundation. There are a variety of degrees possible depending on which aspect of AI you are most interested in. If you already have skills related to AI, your journey into this field may simply be a matter of reinterpreting your resume or getting an additional certification.

Additionally, there are essential soft skills to develop, including communication, critical thinking, and adaptability. A career in artificial intelligence may involve a variety of educational paths, ranging from bachelor’s to doctoral degrees and specialized certifications.

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Newcomers to AI

If you are new to technology in general, you may have more “ground floor” work to do in terms of getting familiar with the science, career fields, and degrees needed to break into AI work.

Breaking into the artificial intelligence field without prior expertise typically requires a minimum of a bachelor’s degree in computer science, data analytics, or a related field. Other adjacent fields may also be useful.

Look for entry-level jobs and learning opportunities. Some may start as a junior data analyst, junior data engineer or junior data scientist, depending on skill level and expertise. One emerging job role? AI prompt designer, AI “trainer” or related work. This involves teaching AI how to manage data, research information and build effective AI models.

General research assistant positions can also offer foundational experience in data handling and analysis. Formal degrees are necessary but just as important? Building practical skills. Newcomers to AI should start personal projects, seek out AI internships and consider competing in “hackathons” or contributing to open-source AI initiatives.

Consider attending coding boot camps or pursuing professional certifications. These may include the IBM Applied AI Professional Certificate or Google Cloud Professional Machine Learning Engineer certification. Continuous learning is essential in this rapidly evolving field to stay competitive, as new tools and techniques emerge daily.

Specialized AI Degrees

Many AI jobs require specific, specialized degrees. Below you’ll find descriptions of some, but not all, degree options in the AI field worth exploring if you are considering a transition out of your current career (military or civilian) and into an AI specialty:

• Artificial Intelligence Degrees: These programs offer interdisciplinary studies that include human sciences, computer science, and engineering, specifically preparing graduates for careers in AI. They may consist of AI-centric courses, such as ethics in AI, and may provide valuable industry internships.
• Machine Learning Degrees: These programs focus on software development that drives intelligent machines, providing additional training in machine-learning based automation, data mining, and predictive analysis.
• Robotics, Engineering, and Autonomous Systems Degrees: These programs may focus more on electrical and mechanical engineering courses than some computer science options. Such courses of study may include designing physical systems and implementing AI to control them.
• Computational Linguistics and Natural Language Processing Degrees: These degrees are for AI careers centered on the autonomous analysis of human language. They provide students with a deep understanding of the complexities of language, which key in designing AI language models.
• Data Science and Analytics Degrees: These programs prepare students for careers that involve utilizing vast amounts of data for predictions and business decisions. The curriculum focuses on the critical skills of obtaining, sanitizing, and effectively utilizing large-scale data.

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The State of the AI Industry as a Career Field

What kind of expectations should you have when considering your role in this expanding career field? The global AI market was valued at $197 billion in 2023. It is projected to grow by 36.6% annually from the time of this press release to 2030. AI-related job postings are increasing 3.5 times faster than the average for all job advertisements.

If you are contemplating a career transition into this field, have a look at our list of top AI careers and the education you need to break into them:

AI Engineer

An AI Engineer designs, develops, and implements AI systems, programs algorithms, trains them with data, and builds models into applications. Daily tasks include data collection, cleaning, processing, algorithm creation, rigorous testing, code optimization, API integration, workflow automation, and system monitoring. Collaboration with product managers and data scientists is essential.

What training is required? You may need to start with a bachelor’s degree in computer science, data analytics, or information technology, is required, with a master’s often preferred for senior roles.

Essential skills include programming (Python, C++, Java), machine learning, deep learning, data analysis, and a strong mathematical foundation. Career progression often starts as a junior data engineer or data scientist.

Machine Learning Engineer

Machine Learning Engineers build, test, refine, and deploy machine learning systems. These systems learn from data, predict outcomes, and enable autonomous operations. Daily tasks involve coding, data analysis, model development, dataset validation, model training, performance optimization, pipeline work, and documentation.

Education typically includes a bachelor’s or master’s in computer science, information technology, engineering, or machine learning. Important skills include programming (Python, R, Java), machine learning, deep learning, data management, and frameworks like TensorFlow, PyTorch, and SciPy. Career progression ranges from junior machine learning engineer to head of machine learning.

Data Scientist

Data Scientists analyze and interpret complex datasets, and their work is used to inform business decisions. Data scientists help make trend predictions, and innovate in the AI field.

Daily tasks involve collecting data, identifying trends, running tests, analyzing results, creating data visualizations, and developing algorithms.

An advanced technical degree, typically a bachelor’s or master’s degree in computer science, data science, or a related field, is often required. A background in pure mathematics can also lead to this role. Essential skills include Python and R, statistical analysis, data visualization, data processing, and machine learning models. Career progression moves from data analyst to chief data scientist.

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AI Research Scientist

AI Research Scientists experiment with and develop algorithms. Their goal is to find solutions to AI problems and may specialize in applied mathematics, deep learning, and computational statistics. Daily activities include brainstorming, designing experiments, analyzing data, refining algorithms, developing mathematical models, fine-tuning parameters, and implementing new ideas through coding.

Degrees in computer science, along with substantial research and development experience, are common requirements. Essential skills include Python, applied mathematics, deep learning, computational statistics, critical thinking, and advanced data analysis.

Natural Language Processing (NLP) Engineer

Natural Language Processing Engineers create systems enabling machines to interact with human language, powering chatbots, translation services, and sentiment analysis. They bridge machine learning models and linguistic understanding. Daily tasks involve developing, testing, and refining language models and algorithms using large datasets.

Responsibilities include data preprocessing, feature engineering, and model evaluation.  Educational pathways typically involve a bachelor’s or master’s in computer science, computational linguistics, or a similar discipline.

Key skills include text mining, machine learning, NLP libraries, understanding human language, and programming.

Computer Vision Engineer

Computer Vision Engineers develop systems that analyze and interpret visual data. This is important for technology related to facial recognition, augmented reality, and autonomous vehicles.

Daily workflow involves designing, implementing, and optimizing systems for object detection, image classification, and video analysis. They work with frameworks like OpenCV, PyTorch, and TensorFlow. Duties may include data collection, model training, debugging, and adapting algorithms.

You’ll need a bachelor’s or master’s in computer science or a related field. Essential skills encompass image processing, deep learning, proficiency with OpenCV and TensorFlow, strong Python programming, mathematics (linear algebra, calculus), and problem-solving.

Robotics Engineer

Robotics Engineers design, build, and program robots, integrating AI to enhance robot functionality for applications like warehouse automation and self-driving vehicles. Day-to-day tasks involve robotics programming, sensor integration, and using/developing machine learning algorithms.

A bachelor’s or master’s degree in robotics, engineering, or autonomous systems is typically necessary.

Essential skills: Specialized robotics programming, sensor integration, and machine learning algorithms. Career progression ranges from robotics engineer intern to director of robotics.

AI Product Manager

AI Product Managers are responsible for developing and deploying AI products. They are part of the entire product lifecycle, from initial idea to final implementation. Their focus is typically on aligning technical capabilities with business goals.

Daily tasks involve orchestrating AI product strategy, design, and deployment. They analyze product performance, market trends, and user data to inform strategic decisions and document roadmaps.

A bachelor’s degree is enough to get a foot in the door, but a strong understanding of AI technologies is key. Necessary skills include communication, strategic thinking, knowledge of AI technology, market analysis, user experience design, and leadership. Interpersonal skills, such as persuasion and adaptability, are also highly valued. Career progression ranges from Associate Product Manager to Vice President of AI Products.

AI Architect

An AI Architect designs the foundational framework for an artificial intelligence system. This role involves the overall management and strategic direction of all technical services functions associated with AI and machine learning applications.

Daily tasks: selecting appropriate AI tools and programs, overseeing the development team, ensuring adherence to regulatory compliance, and seamlessly integrating AI components with existing information technology infrastructure.

A background in computer science (or a related field) is usually required. A bachelor’s degree is often considered the minimum. For advanced roles, a master’s or even a doctorate is frequently sought. Essential skills for AI Architects include expertise in AI-driven workflows, natural language processing, and data management. Other options include data science, infrastructure, and operations.

Proficiency in programming languages (such as Python and Java) is common, as is familiarity with AI frameworks and experience with cloud platforms like Amazon Web Services, Azure, and Google Cloud Platform.

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