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 visible in industries such as finance, security, education, healthcare, and data storage and retrieval.

However, to fully realize its potential, AI must first be integrated into the daily operations of businesses and governments. The process would require extensive coordination and time, which underscores the 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 computer professionals who design and develop data-driven systems and applications with AI and machine learning techniques. They primarily do this to improve the functional and technical operations of businesses.

An example of this is how an AI engineering team can write software that observes customers’ buying patterns. They then train the software using Amazon’s customer data over the years. When they integrate this AI into Amazon’s commercial algorithms, it can make purchase suggestions for customers based on their behaviour, thereby driving up sales figures. This is called predictive modelling.

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 collaborate with product managers to either integrate these models into existing frameworks or synchronize them with applications through APIs (Application Programming Interfaces).
• 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 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.

Typical AI Engineering Roles (Components of an AI Engineering Team):

1. Software Engineer: writes software that AI uses to execute its commands
2. Machine Learning Engineer: responsible for training the AI.
3. Machine Learning Researcher: works on ML innovations
4. Applied Machine Learning Scientist: responsible for bringing ML research innovations to life.
5. Data Scientist: assesses data for insights
6. Data Engineer: responsible for organizing and saving big data used to train and update AI
7. AI Product Manager: decides which AI tech to build based on feasibility and demand.

According to the US Bureau of Labor Statistics, the average annual salary for AI engineers, who fall under the category of Computer and Information Research Scientists, is $140,910. As of April 2025, Glassdoor estimated the average salary for an AI engineer in the US to be $135,000.

However, these salaries are often earned by clear indications 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 a Bachelor’s or Master’s Degree in a tech-related discipline such as Data Science, ICT, Statistics, Computer Science, Mathematics or Engineering. 

Training & Certification:

There are numerous online learning platforms that offer in-depth courses and modules on AI fundamentals and advancements. You can take free AI courses as a beginner looking to start a career in AI. If you’re more familiar with the basics, consider taking more advanced programmes to deepen your understanding, broaden your knowledge, and upskill. 

A few of them are:

AI For Everyone by DeepLearning.ai (Andrew Ng) – for beginners

IBM Applied AI Professional Certificate – Intermediate

Udemyʼs Artificial Intelligence A-Z – Beginner to intermediate

Google Advanced Data Analytics Professional Certification

MITʼs No Code AI & ML Program 

Microsoft’s Training for AI Engineers

Amazon’s AWS AI & ML Scholars Program

Amazon Machine Learning Summer School (currently available only in India) – enables students to acquire practical ML skills under the guidance of Amazon’s scientists.

There are many more online AI/ML courses and programs like these, allowing you to choose one that suits your learning style and pace, whether you prefer to learn independently or with the guidance of an instructor.

At the end of each course or program, your theoretical knowledge and practical capabilities are assessed through a certification exam, which qualifies you to become 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 at 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 Spark SQL, Apache Link & Google Cloud Platform, so as to channel extracted data towards beneficial use.

For Data Modeling, you must be familiar with SQL and NoSQL databases, as well as data processing tools such as AWS S3, Hadoop, and 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 necessary to work with deep learning algorithms and assess the performance of models using metrics such as accuracy, precision, recall, root-mean-square deviation (RMSE), F1 score, and mean absolute error (MAE). 

To stay ahead of evolving threats to data integrity in AI systems, a solid grounding in AI Security will enable you to deploy effective data security measures while also gaining knowledge of regulations surrounding data protection, encryption, and usage. 

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

Tools:

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

Additional Skills: 

These include verbal and written communication skills, critical thinking and problem-solving abilities, as well as the ability to work within a team and coordinate projects. You must also possess niche knowledge that enables you to make informed decisions regarding your area of focus, whether it is healthcare, commerce, education, gaming, economics, or 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 need to pitch to companies and clients with a top-notch resume.

Your resume must be structured and optimized to effectively describe relevant AI/ML skills and tools you have worked with. Highlight your technical and transferable skills, as well as relevant soft skills. Include detailed educational qualifications and training credentials. 

When pitching to businesses, you must highlight the ways you believe AI integration can streamline their operational processes. For example, it improves strategic decision-making through predictive analysis, data regulation, customer service, and feedback analysis, and even the automation of tedious and monotonous business processes. 

Recruiters often seek skilled and experienced professionals with a strong foundation in industry practice. Sites like Indeed, Toptal, LinkedIn, Arc, Guru, Fiverr, Freelancer, PeoplePerHour, Turing, WellFound, and FreeUp are great spaces to get hired or connect with potential clients. 

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

Large companies and top recruiters often prefer AI engineers with extensive experience and exceptional skills. Unsurprisingly, they are likely to overlook educational qualifications and prioritize work experience, especially when such expertise comes highly recommended, accompanied by 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 excellent platforms for networking with other professionals, exchanging insights, discussing trends and evolving techniques, and potentially securing mentorship.

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

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

Social media platforms like Twitter, Reddit, and LinkedIn often host vibrant AI communities where you can join to stay constantly updated on emerging trends, potential collaborations, projects, and lucrative job opportunities.

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 proper training, resources, guidance, and practical experience to achieve mastery. And not only does this field offer stable career paths with a range of possibilities for growth and financial enrichment, but it also provides the opportunity to create a lasting impact on technology and the way we live. 

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 embark on your AI engineering journey!