About Me

I am an AI Engineer and Technical Lead who specializes in designing, building, and deploying production-grade LLM-based systems. I architect autonomous AI solutions that are robust, scalable, and secure-by-design. My goal is to engineer intelligent systems that work in the real world—and to ensure they’re ready for it.

What I Do

My professional focus is on building Agentic AI systems that solve real-world problems at scale. I specialize in:

• Agentic AI Architecture: Designing and deploying autonomous multi-agent systems and RAG pipelines using LangChain, OpenAI API, and Python to automate complex workflows.
• LLM Engineering: Integrating large language models into production applications, applying prompt engineering, fine-tuning, and optimization techniques to maximize performance.
• MLOps & Production: Standardizing the ML lifecycle (training, deployment, monitoring) using MLflow, automating CI/CD pipelines, and orchestrating containerized deployments on Kubernetes.
• AI Security: Implementing “Secure-by-Design” principles, conducting red-teaming exercises (prompt injection, jailbreaking), and ensuring AI systems are robust against adversarial attacks.
• Data Engineering: Building high-throughput data pipelines with Dagster, Apache Kafka, and Elastic (ELK) to power real-time AI analysis and decision-making.

Professional Background

I currently work as a Technical Lead and AI Engineer, where I manage multidisciplinary teams (10-15 engineers) and oversee the end-to-end engineering strategy for AI-driven platforms. My core responsibilities include:

• AI System Architecture: Leading the development of multi-agent systems for automated incident response (SOAR), moving from experimental prototypes to stable, production-ready services.
• Engineering Leadership: Bridging Research and Operations to deliver scalable AI solutions, coordinating cross-functional teams, and driving technical excellence.
• Production Deployment: Architecting microservices on Azure/GCP using Docker, Kubernetes, and DevSecOps workflows to ensure reliability and high availability.
• Data Infrastructure: Engineering complex telemetry pipelines using Apache Kafka, Elastic (ELK), and gRPC to ingest and normalize data for real-time AI inference.

Before focusing on AI, I built a strong foundation in cybersecurity as a Cybersecurity Engineer and as an AI Security Researcher, where I applied ML to security analytics and built detection systems at scale.

Competitive Hacking & Awards

Competitive hacking shaped my problem-solving mindset and continues to inform how I approach AI security and robustness testing.

• 🇪🇸 Spanish National Cybersecurity Team (ESCS): Member & Representative.
• 🏆 Top 1 Spain on CTFtime (2021-2023).
• 🥇 National Champion: Spanish National CTF 2019 (Cybercamp).
• 🌍 International Wins: Top placements in HTB and Incident Response competitions (CyberEx 2021/2025).

Tech Stack

I believe in using the right tool for the job. My daily drivers include:

• AI/ML: Python, LangChain, OpenAI API, PyTorch, MLflow.
• Data & Backend: Apache Kafka, Elastic (ELK), Airflow/Dagster, FastAPI.
• Infrastructure: Docker, Kubernetes (AKS), CI/CD (GitHub Actions), Terraform.
• Security: Offensive Security tools and Adversarial ML frameworks such us Burp Suite, Nuclei, MITRE ATLAS and OWASP LLM Top 10.

My cybersecurity blog: https://ironhackers.es