Published on 6th March 2024
Essential libraries and frameworks
Proficiency in languages such as Python, Java, C++, or R are required in AI Engineering.
Data preparation
Skills like data structure, data cleaning, preprocessing, and feature engineering are important for AI Engineers.
Preparing Models
Skills and understanding of concepts like supervised, unsupervised and reinforcement learning, neural networks, and optimisation techniques
Familiarity with NLP libraries
Understanding NLP and working projects involved text analysis, sentiment analysis, LLM, and chatbots
experience with OpenCv
Worked on projects involving image analysis and video analysis, object detection, and recognition.
benefitial for large datasets
Start gaining knowledge in Hadoop, Spark, and distributed computing frameworks.
Adaptation to Changing Environments
Problem-solving skills help AI engineers identify patterns, devise strategies, and design algorithms to address these challenges.
Safety and Security
An AI Engineer should be aware of ethical and legal considerations around AI and data privacy.