AI Engineer

Multipurpose programming language popular for its simplicity and large ecosystem for data science, backend, and more.

Programming language specialized in statistical analysis and data visualization.

Structured query language used to manage and manipulate relational databases.

Relational and object-oriented database management system known for its reliability and extensibility.

Open-source machine learning library used to develop and train deep learning models.

Open-source distributed processing engine used for analyzing large volumes of data.

Open-source framework for storing and processing large volumes of distributed data.

Google Cloud Platform, a set of Google cloud services including infrastructure, databases, and data analysis.

Amazon cloud services platform offering infrastructure, storage, databases, and more.

Set of techniques and algorithms that allow computers to learn from data and improve their performance on specific tasks without being explicitly programmed for them.

Set of technologies and practices used to store, process, and analyze large volumes of data.

Subfield of machine learning that focuses on using deep neural networks to model and solve complex problems such as image recognition, natural language processing, among others.

Basic knowledge of the principles and techniques of artificial intelligence, including algorithms, machine learning, logic, and expert systems, used to create systems capable of performing tasks that require human intelligence.

Introduction to the principles of relational and non-relational databases, schema design, and normalization.

Study and application of complex algorithmic techniques to solve high-difficulty computational problems, including optimization algorithms, graphs, searches, and machine learning.

Ability to identify problems and develop efficient and effective solutions.

Ability to evaluate and question information logically and objectively to make well-founded decisions.