Artificial intelligence (AI) is having a significant influence across businesses, resulting in a high demand for skilled AI engineers. AI is now one of the most sought-after skills in 2025.
AI is becoming an integral component of everyday problem-solving, whether it's assisting doctors in making faster diagnoses, enhancing how organisations manage daily operations, or making routine chores easier.
As a result, those proficient in AI have their doors open to a wide array of opportunities and career paths in tech. They will get to transform various industries and drive economic growth. AI is creating new job roles, automating tasks, improving efficiency, and enabling innovation across numerous sectors.
Here’s what candidates would need to have a successful career in AI.
Subject matter expertise
To begin with, aspirants must be proficient in a coding language and core mathematical concepts.
To get started with AI, focus on learning a programming language. Python is the most popular language for AI development due to its extensive libraries and frameworks. While Python is dominant, consider learning JavaScript as it can be used for front-end development.
AI aspirants must also be proficient in core mathematics such as linear algebra, calculus, and statistics.
Linear Algebra is important to understand the mathematical foundations of neural networks and other AI algorithms.
Calculus is used for optimisation techniques in machine learning.
Calculus is crucial for data analysis, model evaluation, and understanding probability in AI.
Most importantly, aspirants must also familiarise themselves with fundamental AI concepts like machine learning and deep learning.
Machine learning is developing algorithms that can analyse and learn from data, find patterns, and make predictions or judgements without being explicitly instructed to do so for each instance.
Deep learning entails working with big, unstructured datasets and simulating how the human brain processes information to perform tasks such as picture and speech recognition, natural language processing, and more.
Skillset
Apart from subject knowledge, those wanting to build their careers in AI must also possess a variety of skills.
These skills include:
Data Science: This involves studying data to extract meaningful insights, which can be used to identify trends, predict outcomes, and make better decisions.
Data Management: This involves collecting, organising, protecting, storing, managing and extracting data using SQL or other database technologies.
Data Visualisation: This involves presenting data in easily readable and digestible forms like charts, graphs, maps, and Intelligent User Interfaces (IUIs) to analyse trends and derive actionable insights.
AI aspirants must also be able to work with various open-source machine and deep learning frameworks such as TensorFlow, PyTorch and Keras.
In addition, soft skills such as problem-solving, critical thinking, logical reasoning, adaptability and creativity also help AI aspirants further their careers.
Aspiring AI developers must also consider the ethical implications of AI development and deployment, such as bias and fairness, transparency and explainability, privacy and data security, safety and reliability, and the societal impact of AI systems. These issues are critical for ensuring that AI is created and deployed ethically, benefiting both humans and society.
