How To Become An AI Engineer in 2025

AI research and development have been ongoing since the 1950s. The attention it has received in the last three years makes it seem like novel technology, but it is not.

Science fiction introduced us to the concept of artificial intelligence (AI) in the early 20th century. However, it remained a fantasy topic until Alan Turing published Computer Machinery and Intelligence. This publication became a foundation for the study of computer intelligence. What followed in the years after were key milestones and breakthroughs as computers got smarter, faster, cheaper, and more sophisticated.

With the rise of natural language processing (NLP) and image generation models, significant advancements have been happening in the field of artificial intelligence. 

AI technologies such as ChatGPT have opened up endless possibilities for applications in real life. Some of these applications are seen in industries like finance, security, education, healthcare and data storage/retrieval. But in order to fully realize its potential, AI must first be integrated into the daily operations of businesses and governments. The process would require tons of coordination and time, which means that there is an urgent need for professionals and experts with AI engineering skills.

This is where an AI engineer comes in. 

Who Are AI Engineers and Why Do We Need Them?

AI engineers are professionals who create data-driven systems and applications using AI and machine learning skills. They do this mainly to improve the functional and technical operations of businesses in certain fields. 

An example of this is how an AI engineer can integrate AI into Amazon’s commercial algorithms, creating a chatbot/shopping assistant that can observe customers’ buying patterns and use the data to make purchase suggestions. This is called predictive analysis, and only an AI engineer can train algorithms to mimic human reasoning and interact with customers, just like a regular shopping assistant.

There is so much more that AI engineers can do:

• They can run statistical analysis and use the results to streamline an organizationʼs operational processes.
• They can create AI-based models and work with product managers to either integrate these models into established frameworks, or sync them with applications through APIs (application programme interface).
• They are also capable of managing the development of AI-based infrastructure. They do this by running tests to ascertain the efficiency of AI tools.
• They can also help businesses to navigate the technical and ethical implications tied to consumer rights and AI use. 

Where Can AI Engineers Work and How Much Do They Get Paid?

Due to their educational qualifications, training certifications and technical skill sets, AI engineers are highly sought after. This is because they can specialize in businesses tied to data science, software development, robotics, data engineering, advanced research, machine learning and analytics. They can also work in government agencies and research institutes. 

Big companies such as Google, Microsoft, IBM, Meta, Amazon, Walmart, Bloomberg, PwC and KPMG are working on integrating AI into most of their services. This means hiring more people with AI engineering skills.

According to the US Bureau of Labour Statistics, the average annual salary for AI engineers $140,910. As of April 2025, Glassdoor placed the estimated average salary for an AI engineer at $135,000 in the US.

However, these incredible salaries are often earned and justified by clear shows of competency, skill and innovation. Simply put, to clinch such good pay, you must prove your capabilities, and there is no better way to do this than by starting with your basic qualifications. 

Qualifications, Tools & Skills Needed To Become An AI Engineer

Education:

 It’s best to have an educational background of a Bachelor’s or Master’s Degree in a tech-related discipline such as data science, ICT, statistics, computer science, and mathematics. 

Training & Certification:

 There are a lot of online learning platforms that offer in-depth courses and modules on AI fundamentals. You can take these courses as a beginner looking to start a career as an AI engineer, or if you’re more familiar with basics, take on more advanced programmes to deepen your understanding, broaden your knowledge and upskill. 

A few of them are:

AI For Everyone by Andrew Ng

IBM Applied AI Professional Certificate

Udemyʼs Artificial Intelligence A-Z, 

Google Advanced Data Analytics Professional Certification

MITʼs Artificial Intelligence: Implications For Business Strategy. 

Microsoft’s Training for AI Engineers

There are many more courses and programs such as these on the Internet, and you can choose anyone you can either learn/commit to, at your own pace or under the supervision of an instructor. Typically, to conclude the learning process, your theoretical knowledge and practical capabilities would be assessed through a certification exam which will qualify you to be a certified professional.

Technical Skills: 

These are essential skills in data science, machine learning and programming, necessary for anyone seriously considering a career in AI engineering.

For Programming, you must be adept in demonstrating proficiency with Python, Java, R, C++ & JavaScript, as these are required to develop algorithms, work fluidly with data and run designed models. 

Data Analysis will require your handling of data tools such as SparkSQL, Apache Link & Google Cloud Platform, so as to channel extracted data towards beneficial use.

For Data Modeling, you must have knowledge of SQL and NoSQL databases, and also data processing tools like AWS S3, Hadoop & Apache Spark. Artificial intelligence is built on data, so AI engineers need to be familiar with the rigors of data modeling and engineering. 

An understanding of Machine Learning models is also needed in order to work with deep learning algorithms and assess the performance of models using metrics such as accuracy, precision, recall, root-mean-square deviation (RMSE), FI score and MAE (mean absolute error). 

In order to stay abreast of evolving threats to the integrity of data held in AI systems, a grounding in AI Security will ensure you can deploy solid data security measures, while also having a knowledge of regulations revolving around data protection, encryption and usage. 

Other technical skills to have are in AI & ML services, computer vision, neural networks, data structures, data manipulation and deep learning.

Tools:

 You’ll need a versatile toolkit and essential resources in order to stay ahead in the dynamically demanding field that is AI engineering. Some of these tools include Spinach, DeepCode AI, Python, TensorFlow, PyTorch, Tabnine, Jupyter Notebooks, NVIDIA GPUs, Keras, Docker, Git and GitHub, Apache Spark, OpenCV, and cloud computing platforms. 

Additional Skills: 

These include verbal and written communication skills, critical thinking and problem solving abilities, as well the ability to work within a team and coordinate projects. You must also have niche knowledge that ensures you’re able to make sound decisions concerning your area of focus, whether it is healthcare, commerce, education, gaming, economics and cybersecurity.

How To Get Jobs As An AI Engineer?

As much as there’s significant demand for AI engineers, you’ll still need to stand out in your skills before getting hired. You also have to pitch to companies/clients with a top-notch resume.

Your resume must be structured and optimized to describe relevant experience and key accomplishments, highlight your technical skills and relevant soft skills, while also including detailed educational qualifications and training credentials. 

In pitching to businesses, you must highlight the ways in which AI can be integrated with their established systems to ease their operational processes, as well as improve strategic decision-making through predictive analysis, data regulation, speech and image recognition, customer service & feedback analysis and even the automation of tediously monotonous business processes. 

Recruiters often look out for skilled and experienced professionals grounded in industry practice, and sites like Toptal, Upwork, Arc, Guru, Fiverr, Freelancer, PeoplePerHour, Turing & FreeUp are great spaces to get hired from or connect with potential clients. 

How To Improve Your Skills and Gain Experience As An AI Engineer?

Big companies and super recruiters often prefer AI engineers with a wealth of experience and formidable skills behind them. Unsurprisingly, they are likely to ignore educational qualifications and go for work experience, especially when such experience comes highly recommended with a track record of accomplishments.

Do not fret though. There are steps and tips to ensure you prime yourself and your portfolio for this. These tips will make you stand out in your applications and interviews.

Internships: These are great starting points to gain practical experience in the industry, especially for entry-level.

AI Communities: These are great places to network with other professionals, trade insights, discuss trends and evolving techniques, and possibly secure mentorship.

Open Source Projects: These allow you to collaborate and stay up-to-date with innovations while learning from other professionals and networking with an eye for career-boosting opportunities.

Webinars & Conferences: By attending these, you can learn from and connect with professional experts while engaging in valuable panel discussions, Q&A sessions and related events.

Social Media: Twitter, Reddit and LinkedIn usually host vibrant AI communities where you can join to be constantly updated about popping trends, possible collaborations, projects and lucrative job offers.

Newsletters, Podcasts & Blogs: Subscribe to these in order to keep up with fresh papers, news and events, as reported or analyzed by experts, researchers and industry practitioners. You can check out the latest on OpenAIʼs blog, Analytics Vidhya, KDnuggets, AI Parabellum & MIT Technology Review.

Conclusion

In conclusion, AI engineering is a demanding field that requires a combination of the right training, resources, guidance, and practical experience to gain mastery of. And not only does this field offer stable career paths with a range of possibilities for growth and financial enrichment, but there is always the opportunity to create lasting impact upon technology and life as we know it. 

As someone looking to compete and thrive within the field itself, you will need to constantly brush up on your technical skills, stay creative and open to evolving possibilities, and always seek out ways to utilize your knowledge and talent for the advancement of AI technology, and the benefit of the world at large.

Good luck as you set out or continue on your AI engineering journey!