Designed and implemented the backend architecture for an end-to-end data visualization application, from ingestion and ETL to optimized querying. Built an event-based upload flow and queues (BullMQ + SFTP + Airflow) for file ingestion and processing, enabling asynchronous downloads with signed URLs. Developed APIs in NestJS with a hexagonal architecture, optimized with Redis Cache, reducing response times to milliseconds and allowing CSV and Excel exports. Implemented integration with PostgreSQL using TypeORM, ensuring efficient queries on large volumes of data. Deployed the solution on AWS (ECR+ECS+GitHub Actions) with automated CI/CD pipelines, ensuring reliable releases.

Developed interactive software in Next.js that allowed users to converse with their documents, integrating a backend with RAG for semantic search. Implemented a data ingestion and processing pipeline from Cloud Storage to Data Store Engine (vector database) using Apache Airflow, enabling automation of embedding updates for RAG. Designed and deployed semantic retrieval APIs in Cloud Run, integrated with VectorSearchAI, achieving a scalable, low-latency serverless architecture that allowed us to offer a stable product to clients such as UNACEM, one of the most important business groups in Peru in the construction sector.

Implemented orchestrators using AWS Step Functions, as well as the Serverless Framework for deployment, optimizing and controlling flows related to specific products. Implemented a tool to perform stress testing on APIs using frameworks such as Locust and K6, maintaining the quality and performance of API changes. Implemented high-demand solutions using AWS services such as SQS queues and SNS topics, reducing response times by up to 70%.

Part of the team building artificial intelligence APIs in Python, leveraging FastApi, enabling accurate predictions on insurance quotes, using Cloud Scheduler and Cloud Build to implement automated deployment processes. Automated the creation process of various GCP services such as Cloud Run and Cloud Function, using tools such as Jenkins and Cloud Build following the CI/CD method. Created custom dashboards to monitor API performance using Cloud Monitoring and Amazon CloudWatch, measuring resource usage and error rates, using SonarQube for code quality analysis. Applied TDD patterns, hexagonal architecture, and Jest for unit testing, improving code quality and facilitating automatic deployments through pipelines in Jenkins.

Developed an end-to-end web application in React to digitize business operations, deployed on AWS Amplify, achieving complete digitization of internal processes. Containerized and deployed the application on AWS EC2 with Docker, optimizing costs and ensuring higher performance. Implemented the backend in Node.js with Express.js and Python, developing integrated APIs with a PostgreSQL database hosted on AWS RDS, as well as cloud services to ensure scalability and availability.