AI skills are the key to landing today’s most in-demand tech jobs. From programming and data expertise to machine learning (ML) and soft skills, the right capabilities can open doors to high-paying careers. Learn the top AI skills in demand, explore pathways, and start your journey into artificial intelligence.
Key points
- AI market growth is accelerating, creating millions of new jobs
- Top AI careers include ML engineer, data scientist, robotics scientist, software engineer, and AI product manager
- Career pathways progress from beginner to advanced leadership roles
- Must-have AI skills include both technical and soft skills
- Multiple learning paths: internships, online courses, boot camps, and degrees
10 AI skills you need for a career in tech
Artificial Intelligence (AI) skills are now essential for anyone looking to excel in the rapidly evolving tech industry. With artificial intelligence jobs growing across every sector, learning the right AI skills in demand today can open doors to some of the best-paying and most future-proof careers.
In fact, some of the biggest brands in the world are exploring the best implementations for AI—if it’s not already an integral part of their workflows.
If you’re curious about where AI is heading, take a look at these statistics:
AI still has a lot of growth left to accomplish. But one of the most eye-opening numbers is that 88% of those who don’t use generative AI are unclear about how it will affect their lives. This presents a huge opportunity for you to learn AI skills that will help boost your tech career.
Keep reading to learn more about the top AI jobs and the skills for artificial intelligence mastery that will get you there.
Why AI skills are in high demand in 2025
Experts and major publications in the tech space say that AI shows no signs of slowing down. In fact, according to Bloomberg Intelligence, AI’s market size is expected to grow to more than 30 times its current size in the next decade.
Given these growth numbers, some of the most in-demand skills in today’s job market relate to AI for many reasons.
Firstly, artificial intelligence skills enable businesses to meet the current state of customer service. The digital age has given rise to smarter customers who know what they want and when they want it. As a result, companies must make the purchasing journey feel personal to each customer.
Traditionally, pulling that off would require a lot of company time and resources. But, with AI, businesses can customize each user’s experience (through data-driven insight and AI chatbots, among other tools) to the point where they feel it was built for them alone.
Naturally, businesses can also use AI for data management automation. The primary benefit of this use case is reducing human error when handling large datasets. While that makes businesses in any industry run more smoothly, it’s an indispensable advantage in sectors like finance, where highly sensitive data like credit scores and bank account information are handled daily.
While these trends open tremendous growth opportunities for the technology itself, humans with in-demand AI skills still have their place in the job market. Today’s AI platforms can do amazing things, but such complex technology requires a guiding hand. The people responsible for monitoring and managing these machines must have highly specialized skills and education.
Best AI jobs and careers to pursue in 2025
Designing a long-term career in AI means carefully selecting which path in the AI ecosystem you choose to follow. Many currently available job titles call for the AI skills that are in demand.
Here are some of the top positions to look for. Unless otherwise noted, the salaries listed below are according to Simplilearn.
1. Machine learning engineer
Machine learning (ML) or AI engineers are responsible for the end-to-end management of AI systems. They play an integral role in designing, building, and managing AI models that analyze large datasets and continue to improve and grow through their experience.
Qualifications: This position typically requires a degree in statistics, computer science, or a related field. Additionally, you’ll want to build your proficiency with programming languages like Python, R, and Java. Potential ML engineers would also do well to gain any prior experience they can with machine learning algorithms.
2. Robotic scientist
Robotic scientists are responsible for building robots that emulate human intelligence. Their tasks can range from assembling products to handling hazardous chemicals or assisting in precise medical procedures like surgery.
Qualifications: Robotic scientists generally require a mechanical engineering, electrical engineering, or robotics degree. You’ll also need intricate knowledge of systems engineering, robotics hardware, and computer programming to excel in this role.
3. Data scientist
Data scientists, or data engineers, focus on finding patterns and trends in large amounts of raw data. They depend on algorithms and statistical tools for their work, and companies use data scientist’s findings to make data-driven decisions to propel their business forward.
Qualifications: You’ll typically need a degree in data science, computer science, statistics, or a related field to become a data scientist. Proficiency in programming languages like Python and R, as well as data analytics tools like Tableau or SQL, is a plus.
4. Software engineer
Software engineers in the AI space develop software applications that leverage artificial intelligence or machine learning. These platforms can help businesses:
- Identify cost efficiencies that enable investments in other areas of potential growth
- Streamline development workflows and decision-making
Qualifications: A degree in statistics, mathematics, computer science, or a related field is required. Strong familiarity with programming languages, AI or ML algorithms, and data analysis is also important.
5. AI product manager
AI product managers serve more of an oversight function, monitoring teams that produce AI products from ideation to rollout. Ideally, these individuals possess intricate knowledge of the AI marketplace. They understand industry regulations, technical challenges, and other hurdles production teams might face on their go-to-market journey.
Qualifications: To fill these positions, you must have a deep understanding of both product management and AI technologies. Additional technical background is a bonus.
Top AI skills to learn for artificial intelligence jobs
So, if AI needs humans, and these humans need to be highly skilled, the question becomes, “What specific skills are necessary?” Let’s explore some of these skills:
Technical skills for artificial intelligence jobs
- Programming languages: Python is the best, but R, Java, and C++ are also important based on your job. You’ll need to be proficient in writing clean, efficient code to build and interact with AI models.
- Data expertise: AI is all about data. Understanding data modeling, warehousing, and processing is essential. Familiarity with big data concepts and tools is a plus.
- Machine learning: This is the core of AI. It’s crucial to grasp machine learning algorithms, including deep learning and reinforcement learning.
- AI and ML frameworks: TensorFlow, PyTorch, scikit-learn—these are the tools that will help you build and train your models. Proficiency in these frameworks is a major asset.
- AI deployment and DevOps: Once you’ve built your model, you need to deploy it in the real world. Understanding deployment techniques and DevOps principles is becoming increasingly important.
- AI security: AI systems are vulnerable to attacks. Knowledge of AI security threats and mitigation strategies is a valuable skill.
Non-technical skills for artificial intelligence jobs
- Communication and collaboration: AI projects are rarely solo endeavors. You’ll need to explain complex AI concepts to nontechnical stakeholders and collaborate effectively with team members from various backgrounds.
- Adaptability and continuous learning: The AI field is constantly evolving. A willingness to learn and adapt to new tools and techniques is essential.
- Critical thinking and problem-solving: AI is all about solving complex problems. Strong analytical and problem-solving skills are key.
- Domain knowledge: While not always required, having deep knowledge of a specific industry (e.g., healthcare, finance) can make you a highly sought-after AI specialist.
The specific mix of skills you’ll need will depend on your chosen AI career path. But this should help give you a strong foundation for success in the exciting world of AI.
AI career pathways
Developing the right AI skills can open doors to the most exciting and high-paying artificial intelligence jobs. Here’s a step-by-step roadmap to help you navigate your AI career journey.
Beginner Level: Building Your Foundations
If you’re new to AI, your first step is to master the fundamentals. This means developing the technical foundation that will support more advanced learning. These skills will help you understand how AI works in real-world applications.
At this stage, aim to:
- Learn a programming language such as Python or R
- Build basic data analysis skills (SQL, Excel, Pandas)
- Understand core machine learning concepts
- Explore AI ethics and responsible AI practices
Gaining these skills can open the door to entry-level opportunities. For example, data analyst, AI/ML intern, or research assistant roles. In those positions, you’ll get hands-on experience while continuing to learn.
Intermediate Level: Applying AI in Real Projects
Once you have a strong foundation, it’s time to work directly with AI models. This is where you’ll start building solutions that deliver measurable value. You’ll begin honing your ability to translate data into actionable insights.
Focus on:
- Training and evaluating machine learning models
- Experimenting with neural networks and deep learning basics
- Using AI frameworks like TensorFlow, PyTorch, or scikit-learn
- Developing strong data visualization and storytelling skills
These capabilities can help you step into roles such as machine learning engineer, AI software developer, or data scientist. You’ll tackle more complex challenges and contribute to production-ready AI systems.
Advanced Level: Leading AI Strategy and Innovation
At the advanced level, your role shifts from building models to shaping the direction of AI initiatives. You’ll be expected to combine technical mastery with strategic thinking.
Skills to develop include:
- Mastering advanced deep learning (NLP, computer vision, reinforcement learning)
- Deploying and scaling AI systems with MLOps
- Implementing AI security, compliance, and risk mitigation strategies
- Building deep expertise in a specific industry or domain
With these advanced skills, you can pursue positions such as AI solutions architect, AI product manager, or senior research scientist. Such roles genuinely help define an organization’s innovation strategy.
How to learn AI skills and start a career in AI
Job seekers should look into the following methods for developing marketable AI skills:
Internships
An internship is an opportunity to gain practical, real-world experience on impactful AI projects. You can explore the practical implications of the latest platforms and build up your portfolio of work. Some companies may even offer apprenticeship programs combining the experience of an internship with the structured curriculum of an educational course.
Online courses
Many companies, including some of the leaders in the tech space, offer online resources teaching the ins and outs. Students can choose subjects ranging from data science to machine learning (ML). The courses often use self-study formats, which is great for learning at your own pace. However, additional learning may be necessary without the guidance of an expert or instructor.
Boot camps
Think of a boot camp as a more intensive internship. You’ll gain knowledge in the AI space by working on actual projects and acquiring skills necessary for the job market in a shorter timeframe. Be sure to research boot camps thoroughly, as some have a higher placement rate than others.
College degrees
If you choose a more structured curriculum, the degree you receive would most likely be in a concentration like computer science, math, or data science, limiting the amount of time and experience you would have with practical AI applications.
Interested in a tech job at Intuit?
Leveraging these AI skills can not only help boost your career but also make you more marketable in the present. As this technology grows, these skills will only become more in demand. Spend time learning them, and your transition into today’s AI job market can be smooth.
At Intuit, our strategy is to build an AI-driven technology platform to help power prosperity for small businesses. If you’re interested in being part of our team, visit the Intuit careers page for all AI openings and other technology roles. Learn how Intuit technologists are leveraging AI to solve some of the world’s most challenging financial problems and make a meaningful impact.
Frequently Asked Questions About AI Skills and AI Jobs
What AI skills are most in demand in 2025?
The most in-demand AI skills include programming (Python, R), machine learning algorithms, deep learning, AI/ML frameworks like TensorFlow and PyTorch, and data analysis. Employers also value soft skills such as problem-solving, adaptability, and communication, especially for roles in high-growth fields like AI engineering and data science.
Which AI jobs pay the most?
High-paying artificial intelligence jobs often include AI solutions architect, senior machine learning engineer, and research scientist. These roles typically require advanced technical skills, years of experience, and industry-specific expertise, with salaries reflecting both demand and the specialized knowledge needed to excel.
How can I start learning AI skills without experience?
Start with the basics: learn Python, explore introductory machine learning courses, and build small projects using free datasets. Online courses, coding boot camps, and AI communities can help you gain hands-on practice. Over time, expand into specialized areas like natural language processing or computer vision.