Emiliano Agüero

From backend APIs to AI agents — I build the systems that make AI work in production.

I'm a Python developer specializing in AI systems — from LLM-powered agents and retrieval pipelines to production APIs with FastAPI. I design and build the backend infrastructure that turns AI research into working products.

Stack

AI / ML

LangChainLangGraphLangSmithPydantic AIPyTorchChromaDBPineconeEmbeddingsRAG

Backend & Infra

PythonSQLFastAPIFlaskSQLAlchemyPostgreSQLMySQLRedisDockerKubernetesAWSDigital OceanGit

Frontend

JavaScriptTypeScriptReactJSNext.js

Automation

PyAutoGUIn8nGitHub Actions

Experience

AI Developer / Python Developer

Jul 2023 - Present

Neuronic

  • Led NeuroAI platform architecture for autonomous AI agents with vector DB integration and SQL execution
  • Built RAG pipelines with ChromaDB and Pinecone for document retrieval and question answering
  • Designed FastAPI microservices on Digital Ocean with Docker and Kubernetes
  • Applied prompt engineering and fine-tuning techniques for LLM optimization
  • Automated internal workflows using AI agents

Python Developer

Jan 2023 - Present

sevendatarivers

  • Built automation tools with PyAutoGUI for process optimization
  • Improved team productivity by reducing manual repetitive tasks

Backend Developer

Feb 2021 - Dec 2022

Freelance Projects

  • Built RESTful APIs with Flask and SQLAlchemy
  • Designed database architecture in MySQL and PostgreSQL
  • Managed full development lifecycle with Git and GitHub

Projects

NeuroAI

Problem: Organizations needed a way to deploy autonomous AI agents that could reason over internal documents, execute SQL queries, and integrate with messaging platforms like WhatsApp — without building everything from scratch.

PythonFastAPILangChainLangGraphChromaDBPineconePostgreSQLDockerWhatsApp API

Outcome: An AI agent platform for creating and orchestrating autonomous agents with vector database integration and WhatsApp API connectivity, enabling teams to automate document analysis and multi-step workflows.

LectinAI

Problem: Researchers at UNLP lacked accessible tools to perform computational morphometric analysis on biological tissue samples, requiring manual quantification of lectins that was slow and error-prone.

PythonPyTorchResNet18OpenCVStreamlitDeep LearningComputer Vision

Outcome: A cross-platform computational morphometric analyzer with a custom ResNet18 model built in PyTorch for automatic quantification of lectins in biological tissue, with image processing via OpenCV and a Streamlit interface.

Live Demo