Daniel

Designed, built, and scaled data-intensive applications with Generative AI capabilities, delivering end-to-end solutions that integrated LLM technologies into production environments for enterprise clients. Full-stack development with Python (FastAPI) backend and React frontend, RESTful APIs, and optimized PostgreSQL schemas for large-scale data workflows. Implemented LLM-powered features using GPT-4 and LangChain, built RAG pipelines with FAISS, performed prompt engineering, and contributed to model optimization and deployment strategies. Contributed to scalable, distributed architectures and DevSecOps practices, supporting CI/CD pipelines and secure releases.

Delivered data analytics and ML-driven insights for clients via Python (pandas, scikit-learn), SQL, and BI tools. E-commerce project: analyzed revenue trends, identified top product categories driving 60% of revenue, and uncovered a 15% decline in repeat purchases. Telecom project: built a churn prediction model with 85% accuracy and identified top churn drivers. Lead scoring project: developed a model achieving 82% accuracy to prioritize high-value prospects, improving sales team efficiency.