Software Engineer | Kotlin Multiplatform | AI & Machine Learning

I am a Software Engineer focused on building scalable, multiplatform applications and intelligent data-driven solutions.

My main area of expertise is Kotlin Multiplatform, with experience in Android and cross-platform development using Jetpack Compose and Compose Multiplatform. I work with modern architectures, cloud backends, API integrations, databases, and software engineering best practices.

I am also expanding my expertise into Artificial Intelligence and Machine Learning, working with data pipelines, feature engineering, model training, evaluation, experiment tracking, and model management using the Databricks ecosystem.

I enjoy transforming business requirements into reliable software products, combining software engineering, data, AI, and product development.

Cross-platform enterprise application developed to digitalize fuel registration and management in construction operations.

The application includes role-based access control, operator and equipment management, fuel records, authentication, and cloud data synchronization.

Stack: Kotlin Multiplatform, Compose Multiplatform, Supabase, PostgreSQL, Ktor

Construction services platform where I act as Technical Lead and Full-Stack Developer, contributing to system architecture, mobile development, backend integration, product requirements, and project coordination.

The project involves direct interaction with the client, coordination between development and design, task management, and continuous product evolution.

Stack: Kotlin Multiplatform, Compose Multiplatform, Supabase, Ktor, Figma

Gamified learning platform focused on Software Engineering education, combining learning paths, lessons, challenges, progress tracking, XP, achievements, and performance monitoring.

The project applies software architecture, multiplatform development, cloud services, and gamification to create an interactive learning experience.

Stack: Kotlin Multiplatform, Compose Multiplatform, Supabase, PostgreSQL, MVVM, Clean Architecture

Machine Learning project developed with Databricks, focused on forecasting daily sales by store.

The project implements an end-to-end Machine Learning pipeline covering data ingestion, data quality validation, temporal feature engineering, data leakage prevention, temporal train/validation/test splitting, model training, evaluation, experiment tracking with MLflow, Model Signature, and model registration in Unity Catalog.

Final Model: Random Forest Regressor RMSE: 1043.12 MAE: 659.35 R²: 0.9247

Stack: Databricks, Apache Spark, Spark ML, MLflow, Python, SQL

Automation solution developed during my internship to eliminate repetitive manual processes, automate data processing, and improve operational efficiency through web automation and data analysis.

Stack: Python, Selenium, Pandas, OpenPyXL, Web Scraping

• Kotlin
• Python
• SQL
• Java
• C++

• Kotlin Multiplatform
• Jetpack Compose
• Compose Multiplatform
• Android SDK
• Material Design
• REST APIs
• Ktor
• Retrofit

• Databricks
• Apache Spark
• Spark ML
• MLflow
• Machine Learning
• Feature Engineering
• Model Evaluation
• Data Preprocessing
• Python for Data Science

• Supabase
• Firebase
• PostgreSQL
• Ktor
• Authentication
• REST APIs

• PostgreSQL
• DuckDB
• RoomDB
• SQLite
• SQL

• MVVM
• Clean Architecture
• Repository Pattern
• Dependency Injection
• Koin
• State Management
• Modular Architecture

• Pandas
• NumPy
• OpenPyXL
• Apache Spark
• Web Scraping
• Process Automation
• Data Analysis

• Git & GitHub
• Figma
• Android Studio
• IntelliJ IDEA
• Databricks
• Trello

Besides technology, I am passionate about music. I enjoy singing and playing guitar, flute, and violin.

⭐ Always learning, building, and turning ideas into software.