Alisson Hayasi Da Costa Email & Phone Number

Sao Jose Do Rio Preto, SP, BR

• As ML Platform Engineer @ Feature Platform, my role includes developing and implementing new strategies for efficient data processing, storage, and low-latency data availability at a terabyte scale in both batch and.
• Developed a Go Lang-based REST API, a core component of the Feature Store, enabling low-latency feature retrieval up to 30K RPS with 12ms P99 latency.
• Refactored legacy Python SDKs used by data science teams to build and test their ML features.
• Refactored a custom RedisSink for Apache Spark to handle upsert operations at scale (~500K writes/s).
• Developed a system for monitoring and notification of feature freshness for batch and stream features.
• Developed an event-driven artifacts metadata management service to automate workflows (source tables, jobs, and features) and decoupling architectural components.Additionally, I serve as a technical reference for the.

• As Machine Learning Platform Engineer @ Feature Platform:
• Refactored legacy components used for processing data to improve maintainability, availability, performance, and cost-efficiency.As a Machine Learning Engineer @ Model Management Project:
• Designed and built a solution to automatically collect and present ML model KPI results to stakeholders.
• Designed and built scalable and cost-efficient data pipelines to collect ML model metadata and track the complete ML model lifecycle.
• Improved ML model tracking solutions to be more fault-tolerant and self-recoverable.

• Search and analysis of MLOps tools, in order to identify the maturity and usability of the tool for client projects.
• Design and development of robust and scalable automated machine learning pipelines (including model deployment & serving steps).
• Implement MLOps practices in client projects for developing and improving scalable ML-powered applications.
• Development, improvement and evaluation of machine learning models in the telecommunication field.

São Carlos E Região, Brasil

• Design, development and maintenance of machine learning pipelines (including model deployment & model serving) so that they are robust, scalable and easy to operate.
• Development, improvement and evaluation of machine learning models (e.g., customers segmentation, points of sale segmentation, demand forecast, stock prediction, etc.).
• Development of an internal platform that encapsulates the products and services offered by the company, enabling data scientists to be able to meet demands quickly and objectively.
• Exploratory data analysis for customers to extract information and generate insights.

São Carlos E Região, Brasil

• Research and develop projects related to the classification of non-coding RNAs using deep learning strategies in all stages of the development cycle, from data wrangling to the implementation and evaluation of the.
• Conducted a study on the impact of using different hyperparameters on the performance of deep feedforward networks for the classification of PIWI-Interacting RNAs (piRNAs). The study resulted in the paper "Performance.
• Developed a state-of-the-art classifier whose accuracy is up to 9% greater than the best predictors in the literature, such as piRNN and piRNAPredictor, with a computational cost approximately 8 times lower. These.

São Carlos, Sao Paulo, Brazil

• Defined the schedule, activities and themes present at the event.
• Performed the search and invitation of contacts and professionals responsible for providing the activities defined for the event.
• Organized the Desafio de Programadores (Programmers Challenge Contest) at the 10th UFSCar Computing Week.

Biologists work daily with a huge volume of data. Keeping data organized, analyzing it and extracting relevant information is complex work. Thus, the computer programming becomes an important tool to handle such tasks, enabling a robust and elegant resolution of various problems of analysis and data organization.In order to disseminate this knowledge, I.