How to become an AI engineer

What role does an AI engineer play in a business? 

The role of the AI Engineer is to take a AI prototype and turn it into a reliable product for use in business. Data scientists often build a model to prove an idea works; an AI Engineer will take this model, make it production-ready, scalable and able to be safely operated in large volumes.

This includes developing data pipeline architectures, making sure models integrate well with other applications running in a business, creating and managing real-time monitoring systems so you know how your application/system is performing after you have put it into production, and ensuring the model is retrained if its accuracy is seen to degrade over time.

In 2026, the focus of the position changed from theoretical AI to an Applied AI role; engineers use a large language model in conjunction with actual business processes rather than running experimental research.

This need for qualified professionals is vast. A UK study found that nearly all organisations (97%) reported they had at least one AI-related skill gap. Over 50% cited gaps in technical areas such as programming and data science.

AI engineers help close that gap, working alongside data scientists, software teams, product managers and solutions architects.

Your path to becoming an AI engineer

The most common route to becoming an AI Engineer begins with a foundation of experience through either software engineering, data science or computer science. Once you have some experience, it is typical for individuals to specialise within the field.

A practical path looks like this:

• Build your programming foundations, particularly in Python.
• Learn the fundamental theory behind Machine Learning (ML) and the primary frameworks used today (TensorFlow & PyTorch).
• Get familiar with cloud-based platforms and MLOps best practices for deploying ML models.
• Develop generative AI & LLM abilities and knowledge, including prompt design, fine-tuning and evaluation.
• Earn recognised certification(s) for the field and demonstrate what you can do by building projects that produce results.

AI certifications offered by cloud providers (Google Cloud, AWS, Microsoft Azure), BCS and APMG are highly regarded for AI engineers to develop their skillset. 

Top Skills That AI Engineers Need

• Programming: AI engineers should be able to write high-quality code using python (the most common programming language used) while being well-versed in software development practices.

• Machine learning: Familiarity with a variety of machine learning algorithms, neural networks, and deep learning techniques.

• Frameworks and tools: Experience with a few of the top frameworks such as TensorFlow, PyTorch, scikit-learn, etc. are useful when building AI/ML models.

• Cloud and MLOps: The ability to deploy, scale, and monitor models in cloud environments like AWS, Azure or GCP

• Generative AI: Familiarity with Large Language Models (LLMs), understanding of how to fine-tune them, evaluate their performance, and develop effective prompts.

• Data skills: Building and managing the pipelines that feed models.

• Communication: Ability to explain the trade-offs of an AI system to someone who has no knowledge of technology.

AI engineers need just as many soft skills needed as there are hard skills: The most sought after engineers understand what types of real-world business constraints influence the way companies use artificial intelligence.

Ready to build AI engineering skills? Explore QA’s AI training courses and certifications to find the right starting point.