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Argentina
2+ years of experience
I’m a Mathematics graduate with a strong drive to turn complex ideas into practical, impactful solutions. My experience spans Functional Analysis, Finance, commercial roles (including buyer-side perspective), and serving as a CTO—giving me a true end-to-end vision: from understanding problems deeply to executing both technically and strategically. I specialize in connecting analytical thinking with real-world decision-making. I can translate business needs into clear models, assess financial viability, understand market dynamics from a buyer’s perspective, and guide teams from concept to a working product. What I bring to a hackathon: • Strong problem-solving skills under pressure • Technical leadership and team coordination • Ability to turn ideas into viable, scalable products • Financial and commercial insight (including buyer mindset) • Focus on building solutions with real market fit and impact I’m looking to join a team that wants to build something meaningful, innovative, and grounded in reality. If you need someone who combines math, finance, commercial strategy, and execution—let’s connect.

RAE DecisionOps is an entropy-regulated trust layer for AI agents and business decision workflows. It helps teams convert noisy, incomplete or conflicting data into simple operational recommendations: act, observe or escalate. The system connects structured inputs, APIs, OCR outputs, market feeds, enterprise data and blockchain oracles through a modular pipeline. Before allowing a recommendation to be treated as reliable, RAE evaluates uncertainty, signal quality, confidence degradation, warnings and reproducibility metadata. The goal is not to build a magical predictor. The goal is to reduce overconfidence in weak-data environments and make AI-assisted decisions safer, explainable and audit-ready. The demo exposes a user-friendly interface where operators can define an Execution Context Matrix with variables, values, weights, confidence levels and notes. Business users receive a clear recommendation, while technical users can inspect regulated confidence, uncertainty, warnings and reproducibility hashes. For the AMD Developer Hackathon, RAE demonstrates how AMD infrastructure can support scalable AI-native workflows where performance, reproducibility and trust matter. The architecture is open-core: the public demo, technical walkthrough and reproducible runtime are available for validation, while production-grade decision matrices, scoring templates, enterprise connectors and deployment playbooks remain commercial modules. RAE can be applied to cybersecurity triage, compliance automation, market simulation, enterprise AI governance, agentic workflows and blockchain-connected data systems.
10 May 2026