About
I hold a PhD in Computer Science and Artificial Intelligence from the University of Murcia, awarded with Summa Cum Laude honors. Over the past 4+ years, my work has centered on bridging the gap between cutting-edge AI systems and robust, production-ready software engineering.
My expertise focuses on developing multimodal architectures (combining audio processing with Large Language Models), orchestrating scalable data pipelines, and implementing Explainable AI (XAI) frameworks. I prioritize building reliable, high-availability microservices over simply chasing theoretical benchmarks.
Experience
AI Engineer
Designed and deployed a decoupled microservices architecture (Python, FastAPI, Docker, Celery) for multimodal AI inference, optimizing processing latency across local and cloud infrastructure. Built end-to-end audio processing pipelines integrating ASR (Whisper), speaker diarization, paralinguistic feature extraction, and semantic vector embeddings (FAISS). Applied Explainable AI (XAI) frameworks utilizing SHAP values to interpret fine-tuned BERT models, delivering complete system transparency. Published 5 high-impact, first-author research papers in venues like IEEE Access and Applied Sciences.
Cloud Technical Support Specialist (GCP)
Provided L2 technical and architectural support for enterprise clients on Google Cloud Platform. Handled deep troubleshooting for production workloads involving Compute Engine, core VPC networking, IAM, and Google Kubernetes Engine (GKE). Collaborated with SRE teams to analyze and fix root-cause infrastructure degradation.
Data Scientist & Machine Learning Engineer
Deployed an industrial real-time CNN for automated defect detection, achieving 98% accuracy and sub-100ms inference latency running directly on resource-constrained embedded hardware. Developed optimized LSTM-based NLP systems for semantic complexity analysis. Built and maintained automated, multi-source ETL pipelines for tabular, image, and raw audio data assets.