AI Engineer
Interested in AI engineering training? Learn more about our training solutions and learning paths.
The role of an AI engineer
What does an AI Engineer do?
An AI Engineer is a professional who designs, develops, and implements AI systems. They integrate AI models with software systems to create intelligent solutions that can analyse data, make decisions, and automate tasks.
AI Engineers work at the intersection of data science, data engineering, and domain-specific expertise, ensuring that solutions are scalable, reliable, and alligned with ethical standards. Their role is crucial in transforming AI research into real-world applications.
What is AI engineering?
AI Engineering is the practice of designing, developing, and deploying AI systems. It combines principles from software engineering, data science, and machine learning or other AI products to create scalable, reliable, and ethical AI solutions that solve real-world problems. It's the bridge between AI research and practical applications.
Why businesses need AI engineers
Businesses need AI Engineers to harness the power of artificial intelligence for competitive advantage. AI Engineers build systems that automate processes, enhance decision making and unlock insights from data.
They enable companies to innovate faster, optimise operations, offer personalised customer experiences, and ensure AI systems are built with a human-in-the-loop approach. Integrating AI into business strategies drives efficiency, scalability, and growth.
AI Engineers are essential for future-proofing in a rapidly evolving market.
AI engineer: roles and responsibilities
AI engineers may perform a wide range of tasks including:
- Consuming AI model services and integrating AI into software systems and applications
- Collaborating with data scientists to develop AI models with machine learning, deep learning, and natural language processing.
- Working with data engineers for data processing to clean, transform, and manage large datasets for AI applications
- Automating workflows and data pipelines to and from AI systems and monitoring AI performance
- Optimising AI models for better accuracy and performance
- Deploying AI systems with ethics, governance, scalability and maintenance in mind and in collaboration with domain experts
AI engineering insights
Skills required to become an AI Engineer
Technical skills
AI governance, including ethics, compliance, and data governance processes.
Programming languages, typically Python, R, Java or C++.
Data science and machine learning and knowledge of algorithms, models, and frameworks.
Deep learning, including experience working with different types of artificial neural networks built from machine learning models.
Mathematics and statistics to interpret or develop machine learning or AI model algorithms.
NLP, natural language and text processing.
Cloud computing in AWS, Azure, or GCP: for deployment of bespoke machine learning or AI models, or to consume and deploy cloud-based AI services.
Software engineering and understanding of version control with git and knowledge of APIs.
DevOps knowledge of CI/CD pipelines and containerisation (as a deployment option).
Database management, including experience with SQL and NoSQL.
Soft skills
An Inquisitive approach to studying problems and increasing domain knowledge of the data being analysed and the processes being automated.
Seeking creative solutions and the ability to think critically and find innovative solutions to complex challenges.
Empathy and collaborative attitude to working in mixed specialism teams. The ability to work effectively and communicate well with cross-functional teams including data scientists, developers, domain experts, and business leaders.
Considering ethics, diversity, sustainability, and data & AI governance.
Committed to staying up to date with advances in data & AI techniques and technologies - with the ability to think critically and find innovative solutions to complex challenges.
How to become an AI Engineer
Explore the learning paths that can help you on your path to becoming an AI engineer.
Insights from the experts
“We're entering a future where 80% of our work may be performed by AI. How do we deploy technology capable of learning the most complex cognitive tasks?
“The answer is simple. We develop soft and hard skills needed to work in a hybrid world. Organisations are creating data and AI-driven cultures. They are learning the tools and techniques to successfully utilise AI to reduce toil and increase impact, safely, and effectively. We need to prepare AI skills now, to ensure maximum positive impact for years to come.”
David Pool
Data & AI Development Director
AI certifications and apprenticeships
Looking for more ways to train in data and AI? Discover a wide range of AI certifications, or learn more about becoming an apprentice with QA.
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