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David Font

David Font

AI/ML Engineer 2 años de experiencia

Talento
BarcelonaMiembro desde 20 de abril de 2026
CursorLangGraphRAG pipelinesAI agentsPrompt engineeringRAGASSafe-by-design patternsChromaDBEmbeddingsXGBoostFastAPIDockerAWS EC2GitLab CI/CD
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CursorLangGraphRAG pipelinesAI agentsPrompt engineering

Sobre mí

AI/ML Engineer with hands-on experience building end-to-end LLM systems, RAG pipelines, and AI agents in production. I design, build, and ship — from scoping with stakeholders to deployment and iteration based on real user feedback. I use AI tools daily (Cursor is central to my workflow) and I'm always looking to go further with them. Based in Barcelona, available immediately.

Experiencia

ML Engineer at Multiverse Computing (2026-01 – 2026-03)

  • Developed neural network compression and profiling pipelines for computer vision models (UNet, ConvNeXt, RegNetY) applied to satellite image processing.
  • Worked in an international agile team using Cursor daily, GitLab CI/CD, and AWS EC2/S3.

ML Engineer at Novatronic S.A. (2024-09 – 2025-10)

  • Identified the problem with QA and dev stakeholders, designed the full architecture, and shipped a production LLM + RAG system that automated test case generation from technical documentation.
  • Built the complete pipeline: OCR ingestion, chunking strategy, sentence-transformer embeddings, ChromaDB vector store, FastAPI backend, Streamlit UI — deployed on AWS EC2 with Docker and GitLab CI/CD.
  • Learned: how to design chunking and retrieval for real documents, how to evaluate RAG quality (RAGAS), and how to iterate a system in production based on user feedback. ~40 active users, ~30% reduction in manual effort.
  • Integrated OpenAI enterprise API with structured prompt engineering, few-shot techniques, and safe-by-design patterns. Added human-in-the-loop feedback loop for continuous improvement.
  • Designed and shipped an ML pipeline over ~1.5M monthly transactions for a regulated banking client.
  • Learned: how to handle severe class imbalance (SMOTE), temporal splitting to avoid leakage, and threshold optimization for recall. XGBoost achieved AUC-ROC 0.93, ~25% reduction in false negatives.
  • Built a fully auditable pipeline with explainability (SHAP values) to satisfy compliance requirements.

R&D Researcher at HP Inc. (2023-05 – 2024-01)

  • Data analysis and scientific literature review (50+ publications) within a 4-person R&D team on a biodegradable materials project.

Educación

Master's in AI & Data Science in AI & Data Science – Universidad Europea de Madrid (UEM) (2023 – 2024)

Bachelor's Degree in Chemistry in Chemistry – Universitat de Barcelona (UB) (2014 – 2023)

Habilidades

AI/ML Engineer 2 años de experiencia

Valoraciones

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