Data Scientist with 1+ years of experience building end-to-end ML systems — from raw data pipelines to containerized, production-ready applications.

I work at the intersection of classical ML, NLP, and GenAI, with a focus on shipping things that actually run in production, not just in notebooks.

• ML systems end-to-end — data ingestion, feature engineering, model training, serving
• Semantic search & RAG pipelines — vector databases, embeddings, retrieval
• Containerized applications — Docker, docker-compose, reproducible environments
• Cloud-deployed models — Azure ML, scalable inference

Generates personalized tourist routes in Spain from a natural language description.

• Embeds user input with a multilingual sentence transformer
• Queries Qdrant (vector DB) with geospatial filtering over 10k+ Spanish POIs
• Optimizes the route with Valhalla routing engine (car / walking / bicycle)
• Renders an interactive map + auto-generates a PDF brochure with Wikipedia images
• Fully containerized with Docker Compose — runs locally with one command

ML & Data: Scikit-learn · PyTorch · Pandas · Sentence Transformers · Qdrant
MLOps & Infra: Docker · Azure · pre-commit · pytest
Languages: Python · SQL

• Building production ML pipelines with proper MLOps practices
• Generative AI — LLMs, RAG systems, and AI agents
• Deepening cloud architecture knowledge (Azure)