Employability
Artificial Intelligence Careers
There is a widening demand and supply gap for AI skills, with a significant increase in online AI and Data Science vacancies. Many firms that have vacancies for AI roles had at least one vacancy that was difficult to fill.
An AI professional needs to exploit a broad range of skills and is likely to conduct their work according to a range of both well-established and cutting-edge roles.
Examples of such roles are data scientist, including big data engineer, legal data scientist, and business data scientist, cloud AI engineer, logistic engineer, negotiation engineer, knowledge/graph engineer, robotics engineer, information retrieval and natural language processing applied scientist, as well as algorithmic impact and responsibility scientist.
In such roles, an AI professional may manage and analyse data of different types using a range of technologies, or extract knowledge of different forms from the data.
In addition, they may develop advanced control and coordination mechanisms for robots, or understand the ethical impacts of AI, mitigating risks and ethical harms arising from the deployment of AI. In more detail:
- A data scientist is cleansing data sets for developing, training, evaluating, and optimising machine learning models applied to real-world problems, such as recommending products based on the interests of a user.
- A cloud AI engineer is making use of cloud-based AI and data management services and libraries, deploying and customising pre-designed pipelines on a cloud platform, integrating them with a full application, which often services many customers interacting with their mobile devices.
- A logistic engineer tackles challenging problems related to logistics and sustainability by designing models and algorithms.
- A negotiation engineer is implementing negotiations and taking responsibility for testing, quality assurance, and deployment of negotiation templates.
- A knowledge/graph engineer is designing knowledge representation to model a given application, developing tools to ingest application data and construct knowledge representation, making use of tools to reason about such representation, exploiting graph databases to store and query knowledge representations.
- A robotic engineer is using AI techniques to develop advanced control and coordination mechanisms for robots so that they can coordinate behaviour with other robots and with human stakeholders.
- An information retrieval and natural language processing applied scientist is investigating and developing machine learning and natural language processing technologies to help design the best and most trustworthy personal assistant, or automatically translating online videos.
- An algorithmic impact and responsibility scientist is assessing the algorithmic impact of products, avoid algorithmic harms and unintended data or machine learning side effects, and better serve users and creators.
Recent graduates have found employment within the following job roles and companies:
- Data Scientist
- Cloud AI Engineer
- Logistic Engineer
- Knowledge/Graph Engineer
- Robotic Engineer
- Algorithmic impact and responsibility Scientist
- Information retrieval and natural language processing applied Scientist