AI Engineers design, develop, and deploy AI models that can mimic human behaviour, analyse complex data, and solve problems autonomously. Their work involves creating algorithms, implementing machine learning (ML) techniques, and integrating AI solutions into existing systems. AI Engineers often collaborate with data scientists, software developers, and business analysts to enhance processes and create intelligent solutions that drive business value.

A typical AI Engineer is involved in the entire AI development life cycle, i.e.:

• Business Analysis: understanding business requirements
• Data Engineering: what data do we have to solve the given business problem? This involves transforming and preparing data to be useful for modelling (typically using SQL, PySpark, and other similar software)
• Modelling: which modelling techniques are appropriate to solve this problem with the data we have? Knowledge of ML theory and frameworks is critical for this – understanding overfitting and underfitting, regularisation, k-fold cross validation, evaluation metrics, pytorch/tensorflow/transformers/xgboost, and so on.
• Deployment: once the model is trained and validated, it needs to be deployed into a production environment. This includes integrating the model with existing systems, ensuring it can handle real-time (online) or batch predictions, and setting up monitoring to track its performance over time. Deployment tools and platforms like Docker, Kubernetes, and cloud services (AWS, GCP, Azure) are often used in this stage.
• AI Infrastructure: which involves managing and optimising the infrastructure needed for AI development (training) and deployment. It includes setting up and maintaining the necessary hardware (like GPUs for training models), cloud services, data pipelines, and continuous integration/continuous deployment (CI/CD) processes. Effective AI infrastructure ensures that models can be scaled, monitored, and iterated upon efficiently.

Additionally, an AI Engineer needs to have strong software engineering principles – GIT, CI/CD, unit and functional testing, writing APIs, etc.