Three fast-growing AI careers and the skills you need

What does an AI consultant do? 

AI consultants help organisations identify opportunities to use AI, select appropriate solutions and integrate them into their operations. 

They assess business needs, recommend AI solutions, and guide the development and deployment of AI systems to enhance operational efficiency. As a result, they typically spend more time engaging stakeholders than writing code. 

Typical activities include: 

• Assessing business problems 
• Recommending AI use cases 
• Running AI strategy workshops 
• Defining AI roadmaps 
• Managing implementation projects 
• Measuring business impact 
• Advising on governance and risk 

What skills does an AI consultant need? 

For an AI consultant, the primary responsibility is helping organisations identify where AI can create business value, designing AI strategies, and guiding implementation.  

Unlike an AI Trainer, success depends more on business understanding, stakeholder engagement and strategic thinking than on directly training models. 

The following skills are particularly useful for a career as an AI consultant: 

• Strategic analysis: Consultants assess an organisation's current state, competitive environment, opportunities and challenges to determine where AI can deliver the greatest impact. 
• Needs assessment: Before recommending AI solutions, consultants must identify the gap between current performance and desired outcomes. This ensures AI is solving a genuine business problem. 
• Leadership: AI consultants help organisations navigate significant change. They must create a vision for AI adoption, gain stakeholder buy-in and guide implementation efforts that support long-term business goals. 

What does a machine learning engineer do? 

Machine learning engineers are the people responsible for building and deploying AI systems such as machine-learning models to analyse data and generate predictive insights or other automated outputs. It’s a role that involves close collaboration with data scientists to deploy models into production environments. 

The most technically focused of these three roles, machine learning engineers typically spend a significant portion of their time writing code and deploying systems. 

Typical activities include: 

• Developing machine-learning models 
• Training neural networks 
• Building recommendation systems 
• Creating predictive models 
• Deploying AI applications 
• Monitoring model performance 
• Managing data pipelines 

What skills does a machine learning engineer need? 

For a machine learning engineer, the goal is to design, build, deploy and improve machine-learning systems.  

Unlike AI trainers (who evaluate and improve training data) or AI consultants (who focus on business applications), Machine learning engineers are primarily responsible for the technical implementation of AI solutions. 

The following skills are particularly useful for a career as a machine learning engineer: 

• Programming: This is the core skill underpinning almost everything a machine learning engineer does, from building models and data pipelines to deploying applications and automating workflows. 
• Quantitative modelling: Machine learning is fundamentally mathematical. Engineers use statistical and mathematical models to identify patterns, make predictions and solve business problems. 
• Feature engineering: Effective feature engineering can have a major influence on model performance. Engineers transform raw data into meaningful variables that improve model accuracy. 

Each of these three careers in AI is currently a growing area of employment, but there are distinct differences in skill set and knowledge required for each role. 

AI trainers emphasise evaluation and information quality, AI consultants emphasise business value and stakeholder influence, and machine learning engineers emphasise technical implementation and software engineering.  

The role that best suits you will ultimately come down to the work you prefer doing and the skills you develop as part of your university degree. For example, a career as a machine learning engineer is the most technical of the three job roles and may be particularly suited if you have a background in computer science, data science, mathematics, statistics, software engineering or a related discipline. 

If you enjoy collaborative work and stakeholder engagement, working as an AI consultant would give you those opportunities. However, if you have a strong eye for detail and an analytical mind then you might consider AI trainer as the best-fitting role. 

Across all three roles, it’s important to have a strong understanding of AI and a high level of data literacy, while it shouldn’t be forgotten that communication, collaboration, critical thinking, and adaptability are all vital skills in addition to technical capability.