Artificial intelligence is changing what's possible in technology. AI systems can help analyze medical images, detect suspicious financial transactions, and make sense of human language. But these capabilities don't develop themselves. They depend on skilled professionals who understand how intelligent systems work and how to make them better.
For people with a background in computing, mathematics, or engineering, artificial intelligence careers offer opportunities to apply existing technical skills in new ways. Keep reading to explore a range of AI career paths, skills employers value, and the education and experience that can help you pursue your professional goals.
Key Takeaways
- Artificial intelligence careers include AI engineering, machine learning, natural language processing, computer vision, and research
- AI professionals need strong skills in programming, mathematics, analysis, and problem-solving
- Job responsibilities vary by specialization, industry, and level of experience
- A bachelor's degree can provide entry into some AI-related positions, while advanced education is valuable for specialized technical and research roles
- Graduate study can help professionals deepen their expertise in developing, evaluating, and responsibly deploying intelligent systems
What Careers Are Available in Artificial Intelligence?
Artificial intelligence jobs exist in nearly every major industry. A healthcare organization might need specialists who can develop diagnostic tools, while a manufacturer may use AI to anticipate equipment failures. Financial institutions, transportation companies, and cybersecurity firms face different challenges, but all need professionals who can turn AI capabilities into dependable solutions.
AI professionals do far more than develop models. They test whether those models perform as intended, determine how to incorporate them into existing technologies, and address problems that emerge during implementation. Their decisions can affect the privacy and security of the people who use these systems.
The scope of responsibility generally expands with experience. Early-career professionals may implement established algorithms, prepare datasets, and test model performance under the guidance of senior colleagues. Experienced engineers and technical leads may design system architectures, establish development standards, evaluate competing approaches, and oversee deployment.
Career prospects are encouraging, although federal employment data generally classify AI specialists within broader occupational groups rather than tracking every AI job title separately.
According to the U.S. Bureau of Labor Statistics (BLS), employment of software developers, software quality assurance analysts, and testers is projected to grow 10% between 2025 and 2035. The agency specifically identifies the expansion of AI-related software development as one factor contributing to demand.1 The BLS also projects 22% employment growth for computer and information research scientists during that period, reflecting demand for professionals who develop new computing technologies, including AI.2
Compensation varies considerably by position, employer, location, and experience. For context, the median annual wage for software developers was $135,980 in May 2025, while computer and information research scientists earned a median of $140,300.1, 2 These figures represent broad occupational categories, not a specific AI engineer salary.
Top Roles: From AI Engineer to Computer Vision Expert
Different AI career paths emphasize different aspects of intelligent-system development. With an understanding of these distinctions, you can more readily identify a specialization that fits your interests.
AI Engineer
AI engineers turn artificial intelligence concepts into working systems. They determine which models are appropriate for a particular problem and develop the software needed to put those models to work. They also test performance and make adjustments as systems move from development into practical use.
An AI engineer working in financial services, for example, might help develop a system that identifies potentially fraudulent transactions. Beyond training a model, the engineer must consider reliability, integration with existing systems, and how results will be evaluated.
AI engineering combines software development with machine learning knowledge, making programming, algorithms, data structures, and system design especially valuable.
Machine Learning Engineer
Machine learning engineers develop models that recognize patterns in data and improve their performance through training. They experiment with different algorithms to determine which approaches work best, then refine the models and monitor their performance once they're in use.
In manufacturing, for instance, a machine learning engineer might develop predictive-maintenance models using equipment sensor data. The objective is to identify patterns that suggest a component may fail before that failure disrupts production.
Machine learning engineering often requires expertise in Python, statistics, optimization, and machine learning frameworks.
Natural Language Processing Engineer
Natural language processing (NLP) engineers help computers interpret and generate human language. Their work makes it possible for systems to answer questions, translate text between languages, or find meaningful information in thousands of documents.
An NLP engineer working in healthcare might develop a system that extracts relevant information from large collections of clinical documents. The engineer must consider language ambiguity, model accuracy, and the consequences of incorrect results.
Modern NLP work may involve neural networks, transformers, large language models, and techniques for evaluating generated or extracted information.
Computer Vision Engineer
Computer vision engineers teach intelligent systems to interpret visual information. A system might need to distinguish a pedestrian from a road sign or identify an abnormality in a medical image. Engineers develop the methods that make those distinctions possible.
The challenge lies in making these systems reliable outside controlled conditions. Changes in lighting, image quality, or the surrounding environment can affect what a model recognizes.
AI Research Scientist
AI research scientists investigate new methods for improving intelligent systems. Their work may involve developing algorithms, testing hypotheses, designing experiments, and publishing findings. Unlike roles centered primarily on implementing established techniques, research positions often emphasize advancing the underlying technology.
The BLS reports that computer and information research scientists typically need at least a master's degree in computer science or a related field, although some positions may have different requirements.2
What Skills Do AI Professionals Need?
AI professionals need more than programming proficiency. Their work calls for mathematical knowledge, analytical judgment, collaboration, and an understanding of how technology performs in real-world settings.
Programming and Software Development
Python is a mainstay of AI development, thanks in part to the extensive libraries available for working with data and building machine learning models. Other languages and tools become important depending on the system being developed. For example, SQL helps professionals work with databases, while C++ may be useful when performance is especially important.
Beyond writing code, AI engineers need an understanding of data structures, algorithms, software testing, and the practices involved in building maintainable systems.
Mathematics, Statistics, and Machine Learning
AI professionals rely on mathematics to understand how algorithms work and why they produce particular results. Linear algebra and calculus underpin many machine learning methods, while probability and statistics help engineers make sense of uncertainty in data.
This knowledge allows professionals to make informed decisions about which models to use, how to improve their performance, and when the results warrant closer examination.
Analytical Thinking and Problem-Solving
AI development involves decisions that cannot be reduced to choosing the newest algorithm. Professionals must determine whether a problem is suitable for AI, assess the available data, establish performance measures, and investigate unexpected results. To be effective, a solution cannot just perform well during an initial experiment; it must work under real operating conditions.
Communication and Responsible AI
AI professionals often collaborate with software developers, researchers, product managers, and subject-matter experts. They need to explain technical findings, clarify limitations, and help colleagues understand what a system can reliably accomplish.
Responsible AI is equally important. The National Institute of Standards and Technology's AI Risk Management Framework emphasizes incorporating trustworthiness considerations into the design, development, use, and evaluation of AI systems.3 For AI professionals, that means considering issues such as reliability, privacy, transparency, bias, and risk throughout the development process.
How Do You Get Started in an AI Career?
There is no single route into artificial intelligence, and the best starting point depends on your education, technical experience, and intended specialization.
For someone already working in software development, the transition might involve strengthening mathematical knowledge and gaining experience with machine learning models. A recent computer science graduate may instead focus on developing specialized skills and demonstrating the ability to apply them.
Strengthen Your Technical Foundation
Begin by assessing your proficiency in programming, data structures, algorithms, linear algebra, calculus, probability, and statistics. These subjects support more advanced work in machine learning and intelligent-system development. For professionals researching how to become an AI engineer, a strong foundation in software engineering provides a valuable starting point for developing more specialized AI skills.
Develop Practical Experience
Hands-on experience with AI development can help you build on your existing technical expertise and demonstrate your ability to solve complex problems. You might build a classification model, develop a language-processing application, or evaluate different approaches to a computer vision problem.
A strong project demonstrates how you think as well as what you can build. Explain the problem you set out to solve, why you chose a particular approach, and what you learned when you tested the results. Be candid about limitations, too.
If you're already working in a technical field, look for opportunities to contribute to AI initiatives within your organization. These projects can offer valuable exposure to the challenges of implementing AI in real-world settings.
Choose a Specialization
Explore the responsibilities and qualifications associated with positions that interest you. AI engineering, machine learning, NLP, computer vision, and research share foundational skills, but their specialized requirements differ. Review job descriptions to identify which programming tools, frameworks, mathematical concepts, and development practices deserve further attention.
Consider Advanced Education
Educational requirements vary across AI careers. According to the BLS, a bachelor's degree in computing or a related field is the typical entry-level requirement for software developers, although some employers prefer candidates with a master's degree.1 Research-oriented computing positions more commonly require graduate education.2 A master's program offers experienced technical professionals an opportunity to deepen their understanding of AI algorithms and prepare for more specialized work.
When comparing graduate programs, look for a curriculum that goes beyond applying existing AI tools. Advanced coursework should help you understand how intelligent systems work, how to evaluate their performance, and how to develop them responsibly.
Put Your Technical Expertise to Work in AI
Artificial intelligence offers new ways to apply your technical abilities to challenging problems. New York Tech's Online Artificial Intelligence, M.S. can help you take a more active role in designing the technologies that address them.
Its 30-credit MSAI curriculum builds your understanding of the mathematics and methods behind intelligent systems, with required coursework in machine learning and responsible AI. You'll also undertake an independent research project and choose electives that let you pursue specialized interests, from natural language processing to generative AI.
Delivered 100% online through flexible, asynchronous seven-week terms, the program offers qualified students the opportunity to complete their degree in as few as 10 months.
Explore the admission requirements and career opportunities. Then contact us directly or schedule a call with an admissions outreach advisor to discuss your background, goals, and options for completing the degree.
Sources
- Retrieved on October 27, 2026, from bls.gov/ooh/computer-and-information-technology/software-developers.htm
- Retrieved on October 27, 2026, from bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm
- Retrieved on October 27, 2026, from nist.gov/itl/ai-risk-management-framework