AI Engineer

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.

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 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

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.