Raul Fernando Victorino Mora

• Designed high-quality prompts and multi-step evaluation tasks to assess large language models’ capabilities in reasoning, coding, instruction following, factual accuracy, and complex problem solving. Reviewed model responses for factual accuracy, logical consistency, safety, and adherence to project-specific guidelines and evaluation rubrics. • Evaluated AI-generated responses across technical and analytical domains, identifying hallucinations, logical inconsistencies, missing requirements, incorrect assumptions, and failures to follow task-specific guidelines. • Developed evaluation frameworks, scoring rubrics, benchmark criteria, and reference answers to improve model quality, reviewer consistency, and training data reliability. • Performed detailed reviews of model outputs involving software engineering, algorithms, APIs, debugging, system architecture, and structured reasoning tasks across Python, C, C++, Ruby, and Rust. • Analyzed complex prompts requiring careful interpretation of constraints, evidence-based reasoning, and accurate information synthesis, including tasks involving document analysis and domain-specific evaluation. • Provided structured written feedback explaining model strengths, weaknesses, failure patterns, and improvement opportunities to support AI alignment and model optimization workflows. • Collaborated with AI training teams and reviewers to maintain evaluation standards, improve annotation quality, and ensure consistent application of project guidelines. • Created high-signal training examples and quality assessments that contributed to improving LLM reliability, instruction adherence, and real-world usability.

• Built and delivered scalable full-stack applications across DeFi, e-commerce, enterprise platforms, and AI-powered products, using React, TypeScript, Go, Python, PostgreSQL, and modern cloud-based architectures. • Designed and implemented an AI + DeFi platform end-to-end, leading technical architecture across frontend applications, backend services, APIs, databases, and AI integrations while ensuring scalability and maintainability. • Developed production-grade RAG systems for customer support and analytics, including document ingestion pipelines, PDF/database processing, metadata extraction, embeddings generation, vector search, and knowledge retrieval workflows. • Engineered advanced LLM pipelines with multi-model routing, prompt versioning, tool/function calling, and fallback mechanisms to improve response quality, reliability, and operational efficiency. • Implemented semantic search infrastructure using PostgreSQL and pgvector, enabling efficient retrieval from internal knowledge sources and supporting AI-powered user experiences. • Collaborated with cross-functional engineering and product teams to deliver reliable software solutions, optimize system performance, and improve overall user experience. • Selected PostgreSQL and pgvector for semantic search, enabling hybrid retrieval with low operational overhead. • Collaborated with multidisciplinary teams to deliver reliable products with strong user experience and technical innovation.

• Started professional software engineering career by supporting the development and maintenance of websites, dashboards, and internal business applications for different business requirements. • Assisted senior developers with frontend implementation, backend logic development, database operations, debugging, testing, and feature enhancements across multiple web projects. • Developed web pages, reusable components, forms, administrative panels, and application features while following established coding standards and software development practices. • Participated in troubleshooting and system maintenance activities by identifying bugs, analyzing issues, implementing fixes, and improving application usability and reliability. • Worked with version control systems, development tools, databases, and web technologies in real project environments while collaborating with remote engineering teams. • Created technical documentation, testing notes, and development updates to improve team communication and maintain project knowledge. • Built strong foundations in full-stack software development, problem solving, software architecture concepts, quality assurance, and collaborative engineering workflows. • Built a strong foundation in full-stack development , problem- solving, and software project delivery.