Daniel Ramirezh

Designed and deployed Generative AI solutions using LLMs, RAG pipelines, and prompt engineering techniques, improving knowledge retrieval accuracy by 35% and reducing manual research time by 50%
Built and optimized machine learning workflows with Python, SQL, and cloud-based data platforms, processing 5M+ records to generate actionable business insights and predictive analytics
Developed AI-powered automation tools integrating vector databases, embeddings, and API-based services, increasing operational efficiency by 40% across data-driven workflows
Created data analysis dashboards and reporting pipelines using Python, SQL, and visualization tools, enabling stakeholders to identify trends and make decisions 30% faster
Fine-tuned and evaluated NLP and Generative AI models using performance metrics, reducing model response errors by 25% and improving user experience in AI applications