My name is Erick Gomes, MSc in Computational Physics from Universidade Federal Fluminense (UFF), currently working as a Senior Data Scientist and AI/ML Engineer with years of experience combining academic research, industrial development, and higher education. My professional journey spans from materials physics research to implementation of generative AI solutions in production, including Large Language Models (LLMs), RAG systems, specialized chatbots, and multi-agent architectures.
Throughout my career, I have developed expertise in the complete machine learning lifecycle: from fundamental research to production deployment. I can highlight my experience in specialized model fine-tuning, building robust MLOps pipelines, cloud platform integration (AWS), and implementing monitoring and compliance systems for AI models in corporate environments. My technical background spans from traditional ML algorithms to the most advanced Transformer-based architectures, always focusing on scalability, performance, and business value.
I currently divide my work between the corporate market, where I serve as Staff AI/ML Engineer at Serasa Experian leading AI and machine learning engineering initiatives at scale, and the academic environment, fulfilling roles as Undergraduate Professor at FIAP and Data Science Professor at Ada Tech. In these educational positions, I teach specialized courses in Artificial Neural Networks, Deep Learning, Genetic Algorithms, Generative AI and Advanced Nets, Artificial Intelligence and Machine Learning, contributing to the education of the next generation of AI/ML professionals in Brazil.
My previous experience in the financial market (Klavi, TecBan) gave me a unique perspective on AI application in regulated environments, including developing predictive models for credit scoring, fraud detection, churn prediction, and Open Finance data analysis. This multi-faceted experience - combining academic research, industrial development, and higher education - uniquely positions me to lead complex AI projects that require both technical rigor and strategic business understanding.
Staff AI/ML Engineer - Serasa Experian
Period: Jan 2026 - Present
Leading AI and machine learning engineering solutions focused on scalability, reliability, and business impact.
Data Scientist | Machine Learning Engineer (MLOps) - Mobi2buy
Period: April 2024 - Jan 2026
Location: São Paulo, Brazil (Remote)
- Responsible for the data ingestion pipeline in the MLOps workflow using AWS (S3, Glue, Athena, EC2).
- Development and refactoring of Python code for integration with AWS via SDK.
- Responsible for the MLOps pipeline using MLflow, DVC, Github Actions, Evidentlyai, EC2, Airflow, Prometheus, Grafana, and Terraform.
- Responsible for building, deploying, and maintaining machine learning models:
- Propensity model for the collection squad.
- Sales forecasting model.
- Churn prediction model.
- Responsible for building and maintaining data engineering pipelines using AWS (Glue, Lambda, Redshift):
- Consumption of transactional database (OLTP) for analytical environment (OLAP).
- Consumption of data from API (REST and GraphQL) for analytical environment.
- Development of Chatbots using Generative AI, Langchain, Vertex AI (Agent Builder):
- Using Langchain to integrate with various GenAI models.
- Using Langchain to build Retrieval Augmented Generation (RAG) systems.
- Using Langchain, CrewAI, AutoGen, and Langflow to build AI Agents.
- Building systems with multiple AI agents.
- Agent monitoring.
- Development of agents for Computer Vision.
- Responsible for backlog management and leading Scrum ceremonies.
Data Scientist - Klavi
Period: August 2023 - April 2024
Location: São Paulo, Brazil (Hybrid)
- Development of Churn models for clients of Financial Institutions.
- Experience building dashboards using Power BI.
- Process automation and ETL pipeline construction (Extraction, Transformation, and Loading) for creating tables consumed by other areas.
- Extraction of structured and unstructured data from different sources.
- Use of Python for building machine learning and data analysis projects.
Data Scientist - Tecnologia Bancária (TecBan)
Period: July 2022 - July 2023
Location: São Paulo, Brazil (Remote)
- Data analysis and proof-of-concept development focused on financial information.
- Data structuring to generate strategic business insights.
- Market trend studies, especially in the context of Open Finance.
- Report generation for specific business demands.
- Development of predictive models for default forecasting and online payment fraud detection.
- Application of data balancing techniques to improve model quality.
- Evaluation of classification models using metrics such as AUC, Precision, Recall, F1-Score, and AUPRC.
- API development, deployment, and model deployment.
Main Responsibilities:
- Sandbox construction using regulated Open Finance data.
- Execution of ETL pipeline to load simulated data into the database.
- Creation of investment copilots using LLMs (Large Language Models) and open data from Open Finance.
Graduate Teaching Assistant (Machine Learning applied to Physics) - Universidade Federal Fluminense
Period: July 2023 - December 2023
As a teaching assistant for the Machine Learning applied to Physics course, I played a central role in exploring and applying the fundamental principles of machine learning to solve specific challenges in theoretical and experimental physics.
My technical contribution involved guiding students in implementing supervised and unsupervised learning algorithms, such as regression, classification, neural networks, and clustering methods, to analyze datasets from various sources. Using languages and libraries such as Python, TensorFlow, and scikit-learn, I explored data preprocessing methods, feature selection, and model optimization to extract accurate insights and reliable predictions.
I was responsible for weekly guidance in the graduate course and assisting in the completion of course activities.
Master's Researcher | Data Science Researcher - Universidade Federal Fluminense
Period: March 2022 - March 2025
Location: Niterói, Rio de Janeiro, Brazil
Master's Dissertation:
Read full dissertation (PDF)
Developing my research in the area of machine learning applied to Physics, I have been using best practices in data science to propose new solutions in the analysis of large databases.
Main Results:
- Use of regression algorithms to build predictive models aimed at determining the formation energy of materials and, thus, establishing a set of materials with thermodynamic stability.
- Use of classification algorithms to build predictive models to separate metallic and insulating materials.
- Construction of regression models to predict various properties of insulating materials.
3+ years as Undergraduate Researcher | Data Science Researcher
During my undergraduate research, I developed skills in various computational tools such as shell script programming, parallel computing, and Linux systems. I was responsible for conducting studies related to materials physics through computational simulation, a field that involves producing large amounts of data to be analyzed.
2+ years developing university extension projects
During the extension project, I used embedded systems such as Arduino and ESP32 to produce physics experiments. I was also responsible for coordinating/guiding a group of undergraduate students to produce similar experiments.
2 years as Electronics Technician
During the electronics technician course, I learned about electronic systems and components, which later enabled me to develop projects in data analysis of electronic circuits and embedded systems.
