Bio
Carlos Urteaga is a Principal AI Engineer, AI Architect, and Ph.D. student focused on the operational maturity of LLM-powered AI agents.
He designs and delivers production-grade AI systems that connect research, engineering, and governance. With 10+ years of experience across financial services,education, retail, telecommunications, consulting, and enterprise technology, he has built scalable RAG systems, agentic workflows, document-intelligenceplatforms, and cloud-based AI services for real operational environments.
His work emphasizes the parts of enterprise AI that usually determine whether a system survives beyond a demo: architecture, evaluation, observability,reliability, production readiness, and responsible operational control. He works across the full lifecycle, from ambiguous requirements and technical design todeployment, validation, and cross-functional delivery.
His research explores practical frameworks for governing and operating AI agents, including Agent Cards, Agent Contracts, Agent Types, and Agent Ledgers, withfocus on documentation, accountability, traceability, and measurable operational maturity.
Interests
- Principal-level AI engineering
- AgentOps and LLMOps
- RAG evaluation and observability
- Operational AI governance
- Reliable and auditable AI agents
- Responsible autonomy in multi-agent systems
Collaboration
He is open to research collaborations, applied AI projects, and technical advisory work related to production AI, agentic architectures, operational evaluation,and AI governance.