Daniel Puente

Personal Projects

Reliable, energetic, and resourceful Generative AI Scientist with experience in generative AI, cloud services (Azure & AWS) and Deep Learning since 2019. Ability to develop complex artificial intelligence algorithms in diverse fields. Expertise in identifying business problems and developing solutions based on AI and advanced analytics.

Projects

🧶 Strands Agents + MCP Tutorial — Manim Visualizations

Client: Personal project

GraphRAG Tutorial: Integrating Neo4j with LLMs

Client: Personal project

RAG AutoEval: Automatic Evaluation for RAG Systems

Client: Personal project

Autogen Multi-Agent Ecosystem: Profile Analysis with Free LLMs

Client: Personal project

LangGraph + Mistral: CV Evaluator agent system

Client: Personal project

Fine-Tuning Image-to-Text algorithms with LORA

Client: NTT Data

GNN fraud detection algorithm deployed in a production Azure ML system

Client: BBVA

NFL contact detection applying Computer Vision

Client: NTT Data

LSTM + GANs Lithium-ion battery RUL prediction

Client: Mondragon University

Optimising engine design using multi-objective evolutionary algorithms

Client: LCM

Clothes recommendations applying GNN + Catboost

Client: Lookiero

LSTM for Montecarlo simulation using Hyperas

Client: BBVA

Coevolution evolutionary algorithms for feeder mapping problem

Client: Ormazabal

Stock prediction using Recurrent Neuronal Networks and Prophet

Client: Pc Componentes

Start up valuation using scrapping and ensemble models

Client: Mondragon Corporation

Projects

🧶 Strands Agents + MCP Tutorial — Manim Visualizations Client: Personal project

GraphRAG Tutorial: Integrating Neo4j with LLMs Client: Personal project

RAG AutoEval: Automatic Evaluation for RAG Systems Client: Personal project

Autogen Multi-Agent Ecosystem: Profile Analysis with Free LLMs Client: Personal project

LangGraph + Mistral: CV Evaluator agent system Client: Personal project

Fine-Tuning Image-to-Text algorithms with LORA Client: NTT Data

GNN fraud detection algorithm deployed in a production Azure ML system Client: BBVA

NFL contact detection applying Computer Vision Client: NTT Data

LSTM + GANs Lithium-ion battery RUL prediction Client: Mondragon University

Optimising engine design using multi-objective evolutionary algorithms Client: LCM

Clothes recommendations applying GNN + Catboost Client: Lookiero

LSTM for Montecarlo simulation using Hyperas Client: BBVA

Coevolution evolutionary algorithms for feeder mapping problem Client: Ormazabal

Stock prediction using Recurrent Neuronal Networks and Prophet Client: Pc Componentes

Start up valuation using scrapping and ensemble models Client: Mondragon Corporation