As technology continues to advance, artificial intelligence (AI) has shifted from being ‘nice to have’ to ‘business critical.’ 

A 2024 McKinsey study, found that companies using AI in their core operations improved productivity by up to 40%. This shows the real and measurable impact of AI on business performance. 

Whether you are a business leader seeking a competitive edge or a professional curious about AI careers, one key role keeps standing out, the AI engineer. 

For organisations, this role bridges vision and execution. For professionals, it opens one of the most in-demand career paths of the future. 

In this article, we’ll explore what an AI engineer really does, the key skills involved, and why businesses should invest in AI engineering talent. 

Why is the role of an AI engineer central? 

Organisations in every sector are rethinking how they work. They are improving governance, building AI-focused teams, and finding new ways to turn data into value. 

AI is no longer just a tool. It has become the driving force of transformation, powering progress, differentiation, efficiency, and new revenue streams. Hiring AI engineers means weaving innovation into processes and unleashing long-term performance. 

As AI transforms tech recruitment, professionals are finding new opportunities at the intersection of technology and business impact, particularly in roles like AI engineering that blend software development, machine learning, and deployment expertise. 

So, let us break it down. 

What is the role of an AI engineer? 

Often the term ‘AI engineer’ is used loosely, but in practice, the role is multifaceted. 

At its core, an AI engineer designs, builds, and deploys AI systems that integrate seamlessly into broader business operations. 

AI engineers help companies to: 

  • Optimise operations through predictive analytics
  • Improve decision-making with intelligent automation
  • Enhance products and services through machine learning capabilities
  • Enable data-driven innovation and new revenue streams 

It is a role that blends advancement, logic, and impact creating solutions that can transform how companies work. 

What they actually do: Day to Day 

AI engineer’s responsibilities typically include: 

  • Business problem mapping: Understanding organisational challenges and defining AI use cases that align with business goals.
  • Data infrastructure management: Designing data pipelines that power AI systems with accuracy and reliability.
  • Model development and deployment: Building and integrating models into production environments that serve real users or business processes.
  • Monitoring and optimisation: Ensuring AI systems perform efficiently and adapt as data or business needs evolve.
  • Cross-functional collaboration: Working closely with data scientists, product managers, and executives to align technical outcomes with strategic objectives. 

What makes the AI engineer role different? 

Unlike traditional software engineers, AI engineers understand how data and algorithms influence outcomes. 

Unlike data scientists, they do not stop at analysis. They build and scale solutions that operate in live business environments. 

In short, the AI engineer is the connector, the one who turns AI potential into measurable business performance. 

Essential skills and competencies 

AI engineers combine deep technical capability with strategic business insight, a mix that transforms data into measurable value. 

Here are the key skills that define strong AI engineering talent: 

  • Programming and Framework Expertise: Skilled in Python, R, or Java, with hands-on experience using TensorFlow, PyTorch, and Scikit-learn.
  • Data and Cloud Proficiency: Ability to design data pipelines, manage databases, and deploy models on cloud platforms such as AWS, Azure, or Google Cloud, making cloud recruitment expertise increasingly critical for scaling AI infrastructure.
  • Machine Learning and Deep Learning: Practical understanding of model design, training, validation, and optimisation for production environments.
  • Systems Integration: Competence in integrating AI models with APIs, applications, and business systems for real-time deployment.
  • Analytical and Problem-Solving Mindset: Strong ability to interpret data patterns and align models with business use cases.
  • Business Acumen: Understands ROI, operational metrics, and the strategic goals behind every AI project.
  • Communication and Collaboration: Can translate complex technical results into clear business insights for executives and cross-functional teams.
  • Ethical and Responsible AI Practices: Awareness of fairness, transparency, and compliance requirements in AI applications.
  • Adaptability and Continuous Learning: Keeps pace with evolving AI tools, frameworks, and industry shifts to maintain cutting-edge capability. 

Suggested Read: The importance of AI training in workplace operations. 

Why should businesses invest in AI engineering talent? 

AI engineers are no longer optional hires; they are central to innovation and efficiency. 

Companies that build AI-driven teams often see: 

  • Faster digital transformation and automation of complex workflows
  • Competitive differentiation through predictive analytics and smarter products
  • Improved customer experience and retention driven by intelligent systems
  • Long-term cost efficiency through scalable, self-learning models 

In recruitment and staffing, the demand for AI engineers reflects a broader trend. Businesses are moving from experimenting with AI to embedding it at the heart of their strategy. 

For leaders, hiring the right AI engineers is an investment in future growth and resilience. 

Hiring and career tips 

For business leaders: 

  • Does the candidate have a proven record of deploying AI systems, not just building models?
  • Can they connect business objectives with technical delivery? Have they defined success metrics and measured impact?
  • Do they understand the full lifecycle: data preparation, model development, deployment, and monitoring?
  • Are they capable of collaborating across functions such as product, engineering, analytics, and leadership?
  • Do they prioritise governance, ethics, scalability, and collaboration? 

For professionals: 

  • Build strong programming and machine learning skills, especially in Python.
  • Work on projects that include deployment and monitoring, not just model building.
  • Understand how AI supports ROI, cost reduction, and customer satisfaction.
  • Develop communication skills to explain technical results clearly.
  • Stay updated with ethics, governance, and compliance. 

Conclusion 

The question “What does an AI engineer do?” may sound simple, yet the role is far more than writing code or training models. It is about creating, scaling, and embedding intelligence into business systems so that real value is achieved. 

In the age of AI-led transformation, the AI engineer stands as a cornerstone, turning possibility into performance.  

Now is the time to act. 

Whether you’re building your AI team or exploring new career opportunities, Lorien’s specialist AI recruitment experts can help you connect with the right talent and unlock business transformation.