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Across technology, business, healthcare, finance and other industries, careers that leverage artificial intelligence (AI) range from highly technical engineering and research positions to roles centered on strategy, project leadership, ethics and user experience. Understanding those differences can help you identify a path that fits your level of education as well as your strengths and long-term goals.
What Types of Careers Use Artificial Intelligence?
AI careers generally fall into two broad groups:
- Roles that build or improve AI systems
- Roles that help organizations apply those systems responsibly and effectively
The amount of coding, mathematics and model development involved can vary widely from one position to another.
Technical AI Careers
Technical careers in AI often focus on data, software, algorithms and computing systems. Professionals may:
- Train models.
- Build data pipelines.
- Write production code.
- Test how AI systems perform.
Although requirements differ by employer, these positions commonly call for skills in programming, statistics, machine learning (ML) and data management.
Business, Strategy and Responsible AI Roles
Other careers focus less on building models and more on deciding how AI should be used. Such roles often combine AI literacy with communication, business judgment and industry knowledge. For instance:
- Project managers may coordinate implementation.
- Product designers help shape AI-powered experiences.
- Ethics specialists may assess fairness, privacy and accountability.
12 Careers That Use AI
The following careers exemplify how broad the field has become. Some titles have established occupational data, while newer titles may be grouped under broader occupations in federal labor statistics. Keep in mind that duties, education requirements and compensation can vary by employer, industry and location.
1. Machine Learning Engineer
Machine learning engineers build, train, test and deploy models that allow software systems to learn from data. They often work with:
- Python
- Machine learning frameworks
- Cloud platforms
- Data pipelines
This role can entail turning experimental models into reliable applications and monitoring performance. A bachelor's degree in computer science, engineering, data science or a related field is common, whereas some employers prefer graduate study.
2. Data Scientist
Data scientists collect, clean and analyze datasets to uncover patterns and support decisions. In AI-focused teams, they may build predictive models, evaluate algorithms and explain findings to business leaders.
The U.S. Bureau of Labor Statistics (BLS) reports a median annual wage of $120,230 in May 2025 and projects 35% employment growth from 2025 to 2035. A bachelor's degree is the typical entry-level education, though some employers prefer advanced degrees.
3. AI Research Scientist
AI research scientists study new approaches to machine learning and intelligent systems. Their work may involve designing algorithms, running experiments or developing new ways for computers to solve complex problems.
The closest BLS benchmark is computer and information research scientists, which reflects a median annual wage of $140,300 as of May 2025 and a 22% employment growth from 2025 to 2035. These professionals typically need at least a master's degree in computer science or a related field.
4. Robotics Engineer
Robotics engineers design systems that allow machines to sense, process information and act in the physical world. AI may support:
- Computer vision
- Navigation
- Object recognition
- Autonomous decision-making
Robotics work can merge software engineering with mechanical, electrical or computer engineering in fields such as manufacturing, healthcare and aerospace.
5. Business Intelligence Developer
Business intelligence developers turn organizational data into reports and dashboards as well as decision tools. AI can expand this work through forecasting, automated analysis and natural-language interfaces. The role often requires competencies in:
- Structured query language (SQL)
- Data modeling
- Visualization tools
- Business processes
- Communication as developers translate technical findings for nontechnical stakeholders
6. AI Software Developer
AI software developers build applications that use machine learning models, generative AI tools or other intelligent features. They may:
- Connect applications to large language models (LLMs).
- Create AI-enabled workflows.
- Write application programming interfaces (APIs).
The BLS has identified software development as one occupation likely to be affected by AI while still showing strong projected demand.
7. Natural Language Processing Engineer
Natural language processing (NLP) engineers work on systems that interpret, generate or organize human language. Projects may involve:
- Chatbots
- Search tools
- Speech systems
- Summarization
- Large language models
Common skills include Python, machine learning, deep learning and NLP libraries. Testing is also important because language systems can produce inaccurate or biased outputs.
8. Computer Vision Engineer
Computer vision engineers create systems that analyze images or video for uses such as medical imaging, manufacturing inspection, autonomous systems and retail technology. The work often combines:
- Machine learning
- Image processing
- Neural networks
- Software development
Employers may look for a background in computer science, electrical engineering, mathematics or a related field.
9. AI Data Analyst
AI data analysts use analytics tools and AI-assisted methods to interpret data and identify trends in order to support business decisions. They may work with:
- Dashboards
- Databases
- Statistical tools
- Generative AI systems that help summarize and explore information
Compared with data scientists, analyst roles may involve less model development and more emphasis on reporting, business questions and communicating findings to decision-makers.
10. AI Project Manager
AI project managers coordinate the people, timelines, budgets and risks involved in AI initiatives. They may:
- Define requirements.
- Keep technical and business teams aligned.
- Track implementation.
Advanced programming is not always required, but enough technical knowledge to communicate with engineers, data teams and organizational leaders can be valuable.
11. AI Ethics Specialist
AI ethics specialists examine how AI systems affect people, organizations and society. Their work can involve elements of:
- Bias
- Fairness
- Privacy
- Explainability
- Governance
Professionals may come from technology, law, public policy, compliance or risk backgrounds. Related titles include responsible AI specialist, AI governance analyst and AI risk professional.
12. AI Product Designer
AI product designers shape how people interact with AI-powered products. They may research user needs, design interfaces, test AI experiences and work with product managers and engineers to make systems understandable and useful. Unlike a purely visual design role, AI product design may also require thinking through:
- Uncertainty
- User trust
- Model limitations
- How an interface communicates what an AI system can and cannot do
Skills That Can Support Careers in AI
Careers in AI span a range of responsibilities, and the exact mix depends on the job. Technical depth matters more in some positions, while business knowledge, communication or governance expertise may be more important in others.
Programming, Mathematics and Data Skills
Programming languages such as Python — along with SQL, statistics, linear algebra and data management — can support many technical AI roles. Engineers and data scientists may also use cloud platforms, version control and machine learning libraries.
Machine Learning and Generative AI Knowledge
Professionals working directly with AI should understand how machine learning systems are trained, evaluated and used. Depending on the role, that may include:
- Neural networks
- Deep learning
- Natural language processing
- LLMs
- Model evaluation
In addition, they should understand limitations such as:
- Hallucinations
- Bias
- Data quality problems
- Privacy risks
Communication and Cross-Functional Collaboration
AI projects rarely belong to one team. Clear writing, presentation skills and cross-functional collaboration can be as important as technical knowledge in many roles. This is because:
- Engineers may explain technical tradeoffs.
- Project managers may translate business needs into requirements.
- Ethics specialists may work with legal, security and product teams.
Industry-Specific Knowledge
Industry expertise can make AI skills more useful because the same technology may be applied differently across sectors.
- In healthcare, professionals may need to understand clinical workflows, privacy and patient safety.
- In finance, risk, fraud, compliance and market data can shape AI projects.
- In retail, teams may use AI for forecasting, recommendations, inventory planning or customer service.
Professionals do not need expertise in every technical area, but understanding the problems and constraints of the industry they serve can help them apply AI more effectively.
How to Choose an AI Career Path
Choosing among AI careers starts with understanding what kind of work you aspire to do. Rather than focusing solely on job titles, compare the responsibilities, education requirements and technical expectations behind each role.
Consider How Technical You Want Your Role to Be
If you enjoy coding, mathematics and experimentation, engineering, data science or research may be a suitable fit.
If you prefer coordinating teams, shaping products, managing risk or working with users, roles in project management, governance, ethics or product design may offer a closer match while still keeping you involved with AI.
Match Your Education and Skills to the Position
Review real job postings to see which degrees, tools and skills employers request. Some entry-level roles may be accessible with a bachelor's degree and a strong project portfolio. Research-focused careers often call for a master's degree or doctorate. Graduate certificates can also provide focused study for professionals who already hold a degree and want to build additional AI knowledge.
Look for Ways to Combine AI Knowledge With Industry Expertise
One of the most practical ways to establish a career around AI is to pair technical or strategic AI knowledge with experience you already have. That combination can help you understand both the technology and the real-world problem it is meant to address. For instance:
- A healthcare professional might move toward AI implementation.
- A financial analyst might focus on AI-supported risk analysis.
- A designer might specialize in AI product experiences.
Key Takeaways About Careers That Use AI
Careers that leverage AI are not limited to programmers or researchers. Some positions center on model development and advanced computing; others focus on implementation, analytics, leadership, user experience, governance or ethics.
Education requirements also vary: Some roles commonly begin with a bachelor's degree, while research-intensive positions may require graduate study. Federal outlook data should be interpreted by occupation rather than applied to every AI-related title. The “best” path depends on the kind of work you want to do, the skills you already have and the expertise you seek to build next.
Develop Skills for AI Careers at UC
University of the Cumberlands (UC) offers online graduate options for students and working professionals to strengthen their artificial intelligence knowledge. The online Master of Science in Artificial Intelligence (MSAI) covers machine learning, neural networks and deep learning, natural language processing, generative AI, human-computer interaction and ethics in artificial intelligence. The 31-credit program also includes AI applications, data visualization and a capstone course.
For a shorter graduate credential, the 12-credit, online Artificial Intelligence Certificate credits can transfer into the master's program and includes course topics in:
- Neural networks and deep learning
- Natural language processing
- Generative AI with LLMs
These options can help learners build technical and responsible AI knowledge without guaranteeing a specific career outcome. Request more information today!
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