Valencia, Valencian Community, Spain
With 18 years of experience across Cloud Architecture, Platform engineering, and SRE, my background is rooted in designing and operating reliable, scalable systems in production. I’ve worked extensively on cloud environments, infrastructure automation, and delivery platforms, with a strong focus on security, cost control, and operational stability. More recently, my focus has shifted toward AI-driven systems that operate within real environments - not just generating output, but executing tasks across tools, services, and infrastructure. As a Forward Deployed Engineer at Kubiya.ai, I implemented agent-based systems into production workflows, integrating with the internal platforms and services. These systems were designed with proper governance, access control, and observability to ensure they operate safely and reliably in real-world conditions. What I focus on today: - Designing AI-driven systems that translate unstructured inputs into structured execution - Building orchestration layers across APIs, infrastructure, and internal platforms. - Implementing retrieval-based systems grounded in internal knowledge and documentation - Ensuring reliability through guardrails, approvals, and full auditability I approach AI as a systems problem where integration, control, and consistency matter more than isolated outputs. Based in Valencia, Spain, I work internationally with companies as an independent consultant, supporting both project-based engagements and long-term B2B collaborations.
AI Architect / Architectural Operator Project: Stealth AI Agent Network Designing and evolving agent-driven systems for enterprise business execution. The role focuses on architecture, decomposition, ontology design, and agent orchestration rather than traditional feature delivery, with the goal of building systems that can generate, validate, and improve other software-producing agents. Responsibilities include: - Design agentic systems that create, orchestrate, and evolve other agents across enterprise workflows. - Decompose complex business functions into reusable agent workflows, missions, tools, and execution patterns. - Define agent ontologies, semantic structures, and interaction models to ensure agents operate with clear purpose and context. - Build “agentic layering” patterns where agents can generate, refine, and coordinate other agents. - Architect systems that use AI-assisted development while maintaining semantic correctness, reproducibility, and architectural consistency. - Work with existing agent frameworks and AWS serverless architecture, including Lambda, Step Functions, DynamoDB, APIs, and event-driven workflows. - Connect agents through purposeful agent-to-agent communication patterns to execute end-to-end business functions. - Contribute to reusable/open-source agent components and patterns for the broader agent network. - Collaborate with distributed teams to translate ambiguous enterprise needs into practical, production-oriented agent architectures. - Focus on scalable systems design, enterprise reliability, and architecture-first execution rather than isolated code generation.
• Designed and deployed execution platforms for AI/ML and data workflows, integrating real-time events, internal data sources, and inference pipelines • Built orchestration layers functioning as: workflow engines for operational + data tasks, structured execution pipelines (similar to DAG-based systems like Airflow) • Implemented data-aware execution flows: integrating knowledge sources (RAG over internal systems), enabling context-driven decision workflows • Designed secure execution models (RBAC, scoped credentials) for automated pipelines • Introduced end-to-end observability across workflows: execution traces, logs, metrics, improved debuggability of distributed processes • Enabled end-to-end workflows from data ingestion → context enrichment → model-driven decisions
• Leveraged Generative AI and Agentic workflows to automate internal product development. • Led the migration of legacy observability tools to a modern OpenTelemetry-based stack. • Redesigned telemetry pipelines to enhance signal quality and minimize alert noise. • Investigated production issues, improving reliability and debugging efficiency across services.
Key responsibilities: o Planned and executed cloud-driven projects in AWS and GCP environments. o Developed and delivered high-level cloud infrastructure designs, including Solution Architecture Documents (SAD). o Conducted comprehensive assessments of cloud infrastructure, focusing on security, performance, and cost optimization. o Led a team of four Cloud Engineers, providing technical leadership, guidance, support, and mentorship. o Planned and orchestrated migrations from on-premises to the cloud and between cloud platforms (AWS and GCP). o Engaged in customer-facing interactions, providing technical expertise and solutions architecture consultation. o Led technical discovery sessions to identify customer requirements and align cloud solutions with business objectives. o Provided ongoing technical leadership and guidance throughout project lifecycles, ensuring alignment with best practices and customer needs.
Project: Cloud-based Geoscience Platform powered by Machine Learning and AI frameworks. Selected Contributions: o Provided terraform automation of Azure resources provisioning. o Delivered CI/CD solutions using Azure DevOps stack. o Created and maintained the dockerization of key system components from scratch.