AI Careers: Roles, Salaries, and Certifications to Get You Hired

So, you’re thinking about a career in AI… good move, it’s one of the fastest-growing areas in tech.

But here’s the thing, it’s not all shiny robots and glamorous research labs. Most companies want people who can actually build and deploy AI systems, not just talk about them. That means skills, certifications, and real-world experience matter a lot.

This article covers the major AI roles out there, what they pay, which certifications help you land the job, and how ONLC’s training paths can get you ready.

AI Careers

Artificial intelligence is rapidly reshaping the job market, opening up a world of opportunities for those ready to dive in. As AI technologies become more advanced, the demand for professionals skilled in machine learning, natural language processing, and computer vision is skyrocketing.

But a career in artificial intelligence isn’t limited to just tech companies; AI professionals are needed in nearly every industry, from healthcare and finance to retail and logistics.

Whether you’re interested in developing predictive models, designing intelligent systems, or analyzing massive datasets, there’s a place for you in this dynamic field. The versatility of AI means you can find roles that match your interests and strengths, all while working on solutions that have a real impact on the world. 

As the job market continues to evolve, building expertise in artificial intelligence is one of the smartest moves you can make for a future-proof career.

The AI Roles You Should Know About

There are more roles in AI than most people realize…

The most common are machine learning engineers, professionals who build and train models, handle data pipelines, and often work with cloud platforms like Azure or AWS. There is a growing demand for these professionals in the AI industry.

Then there’s the AI or deep learning specialist, people who go deep into neural networks, computer vision, NLP, or reinforcement learning.

Moreover, the role of a natural language processing engineer is also crucial; these specialists develop NLP systems using AI technology for speech recognition, sentiment analysis, and language translation in applications like voice assistants and text analytics tools.

And yes, there’s even a need for AI product managers, people who understand the tech enough to guide features, manage priorities, and ensure the engineering work lines up with real business goals.

Depending on what you’re good at (or what excites you), you can find your niche here.

AI Skills and Qualifications

Breaking into a career in artificial intelligence requires a blend of technical skills and a strong educational background. Proficiency in programming languages like Python, Java, or C++ is essential, as these are the building blocks for developing AI solutions.

On top of that, a solid foundation in computer science or data science will help you understand core concepts like data structures, statistical methods, and data analysis.

However, to stand out as an AI professional, you’ll need hands-on experience with machine learning algorithms, deep learning frameworks, and natural language processing techniques. Additionally, familiarity with the latest AI tools, including generative AI and cloud computing platforms, is increasingly important as the field evolves.

You can see which credentials are most in-demand in our blog post Best AI Certifications for 2025.”

What the Average Salary Looks Like in AI

Salaries vary a lot depending on location, experience, and specialization. Junior AI engineers often start in the $80k–$100k range in the U.S., while mid-level engineers or specialists can earn anywhere from $120k–$150k.

The average salary for AI roles typically falls within this range, depending on the specific job and level of experience. Senior roles or people working on cutting-edge areas like large language models or medical imaging AI can easily surpass $175k or more, especially at industry leaders in the field.

That said, salary shouldn’t be the only motivator. Early-career roles might pay less but give you invaluable experience that sets you up for six-figure roles later. Many people begin with entry-level positions or entry-level jobs in AI, which are common starting points for building foundational skills.

If you were to ask our opinion, learning opportunities sometimes matter more than the paycheck at first (you build your value fast in this field). Potential employers often look for practical experience, personal projects, and relevant certifications that demonstrate your readiness for these roles.

Other factors that affect salary include your educational background and specialization. Holding a master’s degree or advanced degree can lead to higher earning potential and open doors to more specialized or senior positions.

Certifications That Actually Matter

Here’s where many people spin their wheels, chasing certifications that don’t really move the needle…

If you want credentials that hiring managers recognize, ONLC has some strong options. Many of these certifications focus on essential skills such as software development, which is crucial for building and managing AI applications.

For beginners, the AI-900: Microsoft Azure AI Fundamentals certification is a great starting point. It introduces you to core AI concepts, workloads, and tools on Azure without requiring deep coding experience. From there, AI-102: Designing and Implementing an Azure AI Solution takes you deeper, teaching you how to build and deploy real AI applications using cognitive services, NLP, and computer vision.

If you want a data science focus, DP-100: Designing and Implementing a Data Science Solution on Azure gives you practical, hands-on skills for model training and deployment at scale. I know we already said it above, but gaining hands-on experience is essential to reinforce what you learn and demonstrate your abilities to employers.

ONLC also offers Generative AI training and certification courses for those interested in ChatGPT, Copilot, or HuggingFace model fine-tuning; skills that are increasingly in demand, especially in content creation, where AI tools are transforming digital workflows.

You can explore all of these (and more) at ONLC Courses.

Challenges People Don’t Talk About

Here’s the part many blogs gloss over.

Early on, you might feel like an impostor. Everyone does. AI is a huge field, and no one knows everything. The key is to keep building small wins (like projects, certifications, deployments), so you see progress. Developing strong problem-solving and strategic thinking skills is essential for overcoming these challenges and advancing in your AI career.

Another challenge? The tech changes fast. A framework or model popular today might be outdated in two years. That’s why it’s important to learn fundamentals first and stay adaptable. The complexity of AI systems often mirrors aspects of human intelligence, such as learning, reasoning, and problem-solving.

Also, early-career AI roles aren’t always glamorous. You might spend weeks cleaning messy data or maintaining existing models before you get to build something new. That’s normal. Experience compounds quickly in this field, so those first roles matter even if they’re not your dream job. Working on real-world projects helps you identify meaningful patterns in data, which is a valuable skill for any AI professional.

Tech changes fast… fundamentals first. Be sure to check out our blog How to Learn About Generative AI to see how new tools fit in with foundational knowledge.

Frequently Asked Questions

Do I need a computer science background to start a career in artificial intelligence?

Not necessarily, but a computer science foundation does help. Many artificial intelligence careers benefit from familiarity with data structures, algorithms, and programming concepts. However, with the rise of beginner-friendly courses and certifications, it’s possible to start in a related field—like data analysis or business intelligence- and transition into AI by building your AI skills over time.

What AI skills should I learn first?

If you’re new, start with Python programming, as it’s the most widely used language in AI. From there, learn the basics of machine learning algorithms, including both supervised learning and unsupervised learning approaches, before moving into more advanced deep learning methods and specialized areas like generative AI. Strong data analysis skills and knowledge of cloud computing platforms like Azure or AWS will also help you deploy and scale AI solutions in real-world settings.

How is machine learning used in business intelligence?

Machine learning plays a big role in modern business intelligence by automating pattern detection, forecasting trends, and turning massive datasets into actionable insights. With machine learning techniques, businesses can uncover relationships in data, improve customer experiences, and support data-driven decision-making without requiring constant manual analysis.

How do machine learning engineers use programming languages?

Machine learning engineers rely heavily on programming languages like Python, R, and sometimes Java or C++ to build machine learning models, automate data pipelines, and integrate AI into production systems. Python remains the top choice because of its simplicity and the strength of its AI ecosystem, including libraries like TensorFlow, PyTorch, and scikit-learn.

In Conclusion

Breaking into artificial intelligence isn’t about ticking off one course or reading a single book; it’s about steadily building the right AI skills, from machine learning fundamentals to deep learning expertise, and pairing them with real-world projects and industry-recognized certifications. Whether you’re starting from a computer science background or coming from a completely different field, there’s a path forward if you approach it strategically.

ONLC’s training programs make that path clearer by offering Python programming, cloud computing, data analysis, and artificial intelligence courses designed for learners at every level. Our certification prep for AI engineers, machine learning engineers, and other AI professional roles gives you both the technical foundation and the credentials employers want to see…