Multilingual AI Engineer (English/Spanish/Japanese) specializing in LLM optimization, prompt engineering, and scalable application development. Experienced in AI model evaluation, data analysis, and cloud solutions using Java, PHP, C#, JavaScript, and Python
My professional journey in software engineering, full-stack development, and intelligent systems
Contributed to advanced software engineering and AI model evaluation projects by building, reviewing, testing, and validating code solutions across multiple programming languages. Designed full-stack applications, backend services, REST APIs, automated test suites, and reproducible Docker environments using C#, Java, PHP, JavaScript, TypeScript, and Python. Focused on code quality, functional correctness, prompt engineering, and production-ready software delivery.
Designed and developed full-stack modules, backend services, APIs, and intelligent features for ERP and business management platforms. Worked with PHP, C#, Java, Python, JavaScript, Node.js, Spring Boot, Laravel, SQL databases, and Docker to build scalable, maintainable, and data-driven systems. Implemented machine learning models, financial analytics, automated reporting, chatbot integrations, and cloud-based services for real-world business operations.
Developed and advised software projects focused on web applications, IoT dashboards, data processing pipelines, NLP systems, and cloud-based platforms. Built backend APIs, authentication services, data analysis workflows, and real-time monitoring solutions using Python, JavaScript, Node.js, SQL, AWS, and Raspberry Pi. Supported technical implementation, system architecture, testing, and deployment for academic and independent software projects.
Companies and labs I contributed to through Outlier AI
Multilingual professional communication
Professional credentials & qualifications
TOEFL/IELTS Certified
Language ProficiencyBachelor's Degree
Academic DegreeGoogle Data Analytics, AWS Cloud Solutions Architect Specialization, IBM Technical Support Professional Certificate
Technical ExpertiseIssued by Outlier · Feb 2026
Recognized for maintaining high quality standards, accuracy, and consistency in AI model evaluation and annotation workflows.
AI coding evaluation project focused on teaching software agents to solve real-world development tasks. Designed clear technical prompts, built complete reference implementations, and created validation workflows using automated tests, Docker environments, and expert rubrics. The project covered full-stack applications, APIs, simulations, dashboards, and production-ready code solutions.
Technologies: Python, PHP, JavaScript, TypeScript, Java, C#/.NET, Docker, Pytest, Vitest, xUnit/NUnit, SQLite, Bash, Git, Cursor IDE
Implementation of a Model Context Protocol (MCP) Server for Idrix Technology, focused on integrating AI capabilities into enterprise software systems. The solution connects business data with intelligent assistants to support data analysis, automated reporting, financial insights, and decision-making workflows. The project combines backend development, API integration, and AI-assisted business intelligence using Java, TypeScript, and server-side architectures.
Multi-Platform SDKs:
Machine Learning API for Idrix Technology built with Python and FastAPI to support financial predictions, model training, and business analytics. The application follows a modular backend structure with clean architecture principles, dependency injection, and RESTful endpoints for real-time prediction workflows. Designed to integrate with ERP systems, SQL databases, and enterprise reporting modules.
Generated structured software engineering trajectories for training AI coding agents. Worked on repository migration tasks involving .NET Framework to .NET 6/8 and Java 8/11 to Java 17/21. Each trajectory documented developer reasoning, code exploration, edits, builds, and test validation using Docker-based environments.
Technologies: C#, .NET 6/8, Java 17/21, Maven, ASP.NET Core, gRPC, Docker, Git, Cursor IDE, xUnit, NUnit, MSTest, JUnit 5, Mockito, Bash
Designed reproducible evaluation environments to measure how well AI models solve real code issues extracted from GitHub pull requests. Built Docker-based test labs, automated validation scripts, golden patches, and structured rubrics to verify whether model-generated solutions preserved existing behavior while improving code quality.
Technologies: TypeScript, PHP, JavaScript, Java, C#/.NET, Python, Docker, Bash, Git, GitHub PRs, Unified Diff, JSON, CI/CD, Benchmarking
AI code review evaluation project where I acted as a senior software engineer to test model capabilities in reviewing pull requests. Selected challenging open-source PRs, wrote technical golden reviews, and designed rubrics to evaluate whether AI models could detect logic bugs, security risks, performance issues, and maintainability problems.
Technologies: Python, Django, JavaScript, TypeScript, Java, C#, PostgreSQL, MySQL, MariaDB, SQL Server, GitHub PR Reviews, Markdown, Prompt Engineering
QA engineering project focused on creating professional test suites for AI coding agents. Analyzed real GitHub issues and commits, wrote clear problem statements, documented public interfaces, and developed tests that failed before the patch and passed after the correct solution. The work validated observable software behavior across backend, frontend, and full-stack repositories.
Technologies: Python, JavaScript, React, PHP, C#/.NET, Ruby, Pytest, Jest, Mocha, PHPUnit, xUnit, NUnit, RSpec, Docker, Git, Bash
Evaluation project focused on testing Gemini's capabilities in statistical reasoning, financial analysis, chart generation, and complex analytical tasks. Built reproducible notebooks, validation scripts, solution artifacts, and detailed rubrics to assess numerical correctness, business logic, visual quality, and dataset attribution.
Technologies: Python, Jupyter Notebooks, Pandas, NumPy, Matplotlib, Seaborn, scikit-learn, statsmodels, Pytest, Markdown, Bash
Designed complex prompts and weighted rubrics to evaluate ChatGPT's performance in advanced data analysis tasks. Curated multi-format datasets, created analytical challenges involving statistics, predictive modeling, visualization, and documented model failures through red-teaming and ground-truth Python solutions.
Technologies: Python, Google Colab, Pandas, NumPy, Matplotlib, Seaborn, SciPy, Scikit-learn, CSV, TSV, XML, JSON, Prompt Engineering
Software quality and AI evaluation project focused on testing Large Language Models' ability to generate reliable code, simulations, and interactive software artifacts. Worked on reviewing, debugging, validating, and improving generated solutions across Python, JavaScript, Java, C#/.NET, and PHP. The work involved identifying runtime errors, logic issues, performance problems, and code quality weaknesses, then creating production-ready reference solutions.
Technologies: Python, JavaScript, Java, C#/.NET, PHP, Code Review, Automated Testing, QA, Prompt Engineering
LLM evaluation project focused on system prompt engineering, tool execution validation, and AI behavior testing. Designed structured prompts, evaluated model responses, identified execution errors, and created clear assessment criteria for correctness, reliability, and instruction following. This work strengthened my ability to combine software engineering practices with AI-assisted development workflows.
Technologies: LLM Evaluation, Prompt Engineering, MCP Tools, System Design, Quality Assurance, Python, JavaScript
AI evaluation project designed to improve model performance in multi-step tool-calling tasks. Developed rubric-based assessments to measure reasoning quality, task completion, tool usage, and response reliability. The project involved analyzing complex AI workflows and validating outputs with structured technical criteria.
Data analysis project using Twitter data, NLP, and geospatial processing to identify streets with high accident incidence in Quito. The system processes unstructured text, extracts relevant traffic accident information, and supports prevention strategies through location analysis and visual insights.
Research and visualization project analyzing more than one million health records from Ecuador's Ministry of Health. Developed in R Studio with an interactive Shiny dashboard, including geographic maps, disease distribution charts, and statistical visualizations for public health analysis.
Power BI analytics solution for insurance and financial data analysis. The dashboard provides interactive views for insured amounts, loans, poverty indicators, customer segmentation, and strategic business intelligence reporting.
Power BI solution with live MySQL integration for real-time financial monitoring. Includes revenue analysis, income vs. expense metrics, customer activity, payment method distribution, and business performance indicators.
Worked on the backend, cloud infrastructure, and data layer of iDrix Contable, an ERP SaaS platform for business management. Developed backend modules, dashboards, REST APIs, multi-tenant database workflows, inventory management features, order synchronization, KPI visualization, and accounting reports.
Technologies: PHP, Laravel, C#, ASP.NET, Java, Spring Boot, Python, AWS, EC2, S3, Lambda, API Gateway, MySQL, MariaDB, Bash, Certbot, REST APIs, Git, Linux
Configured and deployed cloud infrastructure for the iDrix Mobile ERP application, supporting production environments for business users. Integrated mobile clients with backend services through API Gateway, configured routes, validated endpoints, and ensured secure communication between the mobile frontend and backend systems.
Technologies: AWS, EC2, S3, Lambda, API Gateway, React Native, REST APIs, CORS, SSL/TLS, Certbot, Linux, Git
Interactive language learning web application for Japanese vocabulary study. Built with PHP Laravel for the frontend and data presentation layer, with a Python backend for word search processing through the Jisho API. Includes word meanings, pronunciation support, and visual learning features for kanji practice.
Cloud-native invoice management system designed for Ecuadorian microbusinesses. The platform uses AWS services, backend APIs, and intelligent automation features to support billing, customer records, business operations, and financial management workflows.
Cloud-connected IoT monitoring system integrating Raspberry Pi 4 with backend APIs and AWS services. Built with Python, Flask, Node.js, Express.js, AWS Cognito, and AWS Elastic Beanstalk to support user authentication, real-time sensor data collection, and dashboard visualization.
Industrial automation prototype programmed in C using a PIC 16F877A microcontroller. Features Bluetooth and serial communication, multiple safety modes, configurable operation parameters, and user-controlled welding behavior for educational and technical demonstration purposes.
Flask-based web application for offensive language classification using NLP and machine learning techniques. The system processes text input, extracts linguistic features, and applies classification models for real-time content moderation and analysis.
Full-stack business intelligence platform with Bootstrap frontend, Node.js API, and PostgreSQL database. Includes CRUD operations, REST endpoints, interactive Highcharts visualizations, and multi-format report export to Excel and PDF.
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