Marcelo Eduardo Rodrigues Da Silva Filho Email & Phone Number

Brazil

São Paulo, São Paulo, Brasil

Porto Alegre, Rio Grande Do Sul, Brasil

– Development and maintenance of REST APIs and Python services for receiving AI data, primarily using Flask and FastAPI, as well as tools such as SQLAlchemy and Alembic.– Proficiency and comfort in data modeling and relational databases, with an emphasis on PostgreSQL.– Data reception, processing, and display in web platforms built using React.– Proficient in planning, designing, and implementing new products and solutions, I have assisted in the creation of 2 new products since joining the company.– Hands-on experience in enhancing the performance of existing APIs through practices that include optimizing SQL queries, improving algorithms asymptotic complexity, implementing caching with Redis, and transforming recurring tasks into asynchronous ones using pub/sub patterns and message brokers like RabbitMQ.– Proficient understanding and hands-on experience with AWS services, including EC2, S3, and RDS.– Implementation of CI/CD pipelines in the Azure DevOps environment to streamline the review and deployment of new features.– Conceptualized and implemented telemetry dashboards using Grafana to monitor the performance and utilization of servers and APIs, saving time and generating value by identifying bottlenecks of multiple systems.

– Learning the fundamentals of web development.– Developed familiarity with using Docker and Git in my daily work.– Development of features for a large-scale application (movie social network), primarily using the Django framework (Python).– Study and mapping of software infrastructure, including reverse proxies, CDNs, load balancers, and other components.

Presidente Prudente, São Paulo, Brazil

The main objectives of this work were to study and analyze Machine Learning techniques and models, thus obtaining a theoretical foundation and a better understanding of them, in order to develop recommendation models. We then interpreted and compared these models, reaching a conclusion about their behavior and identifying the one with the most potential for practical application, considering the problems observed in each one. To achieve these objectives, various techniques were analyzed and data manipulations were performed on a dataset, enabling the construction of these recommendation systems.