Data & AI Engineer | ML Production Systems Specialized in intelligent systems, production ML pipelines, and data quality automation. My work focuses on the intersection of anomaly detection, ML explainability, and scalable data engineering.
Education:
- 🎓 MS in Robotics & Artificial Intelligence: Universidad de León (8.7/10, Thesis 9.2/10 honours) – Multimodal physiological signal processing for stress detection (EEG, ECG, electrodermal activity)
- 🎓 BS in Computer Science: Universidad de Cádiz – Emergency vehicle detection in urban environments using Deep Learning
Current Role:
- 💼 Data & AI Trainee @ HORSE (Renault Group) | Madrid, Spain
Leading development of ADQG (Autonomous Data Quality Guardian) — a production ML anomaly detection pipeline on GCP/BigQuery- Deployed Isolation Forest, HDBSCAN, Fuzzy K-Means, and autoencoder-based anomaly detection models
- Built full ipywidgets configuration UI with dynamic BigQuery column selection and cost estimation
- Implemented SHAP explainability (beeswarm, waterfall, heatmap plots) for model interpretability
- Designed Bronze/Silver/Gold medallion architecture with Dataform SQLX pipelines
- Migrated workloads from Cloud Run Jobs to Cloud Batch with Artifact Registry containerization
- Built Teams webhook alerting with Adaptive Cards; resolved GCP org policy violations for Vertex AI Workbench
- Developed Gradio and ipywidgets interfaces for production ML configuration
Experience Highlights:
- Production ML: End-to-end pipeline development (data ingestion → modeling → explainability → monitoring)
- Data Engineering: BigQuery, Dataform, GCP infrastructure, Cloud Batch orchestration, IAM/service account management
- ML Models: Isolation Forest, HDBSCAN, Fuzzy K-Means clustering, autoencoders with PCA preprocessing, SHAP interpretability
- Infrastructure: GCP (Vertex AI, Cloud Run, Cloud Batch, Artifact Registry), Teams/Slack integrations, LZ1→LZ2 migration
- Technical Depth: Sigmoid normalization for anomaly scores, cost optimization, data sufficiency validation
Research & Publications:
- 🔬 Peer-reviewed publication: Neural Audio Classification for Emergency Vehicle Detection with Feature Compression via Convolutional Autoencoders
Technical Stack:
- Languages: Python, SQL, C++, JavaScript, MATLAB
- ML/Data: scikit-learn, Isolation Forest, HDBSCAN, SHAP, pandas, NumPy, PCA
- Cloud & Infrastructure: GCP (BigQuery, Vertex AI, Cloud Batch, Cloud Run), Docker, Cloud Storage
- Data Pipelines: Dataform (SQLX), dbt concepts, medallion architecture
- Interfaces: ipywidgets, Gradio, Teams webhooks, Adaptive Cards
- Other: ROS, React, Git
Languages:
- 🌍 Spanish (Native)
- 🌍 English (Professional Working Proficiency)
Location & Availability:
- 📍 Madrid, Spain (Originally from Cádiz)
✈️ Open to relocation / Remote roles- 🎯 Actively exploring: PhD programs (AI/ML research focus), production ML engineer roles, and AI research positions
Interests: Deep Learning, Computer Vision, LLMs, Anomaly Detection, ML Explainability, Reinforcement Learning, Generative AI
Looking to Collaborate On: Open Source ML & Robotics projects, production ML systems, anomaly detection applications
Current Goals: Building a generative AI research portfolio for PhD applications and senior ML engineer positions. Actively exploring: production LLM evaluation harnesses, percussive audio diffusion models, and sparse autoencoder analysis.
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