Architected and deployed Retrieval-Augmented Generation (RAG) pipelines with integrated vector databases for financial and government clients, enabling AI systems to deliver highly contextual responses and improving retrieval accuracy by 40%+.

● Engineered and delivered AI-driven microservices that automated key workflows for enterprise platforms, reducing manual workloads by up to 70%, enhancing operational efficiency, and accelerating product release cycles.

● Designed and integrated Large Language Models (LLMs) into enterprise platforms, expanding feature capabilities while reducing critical process response times by ~60%.

● Led end-to-end database optimization initiatives for tax and disbursement systems in regulated financial environments, achieving 50% faster query performance and lowering cloud costs.

● Developed a high-accuracy ensemble sentiment analysis model of 20 neural networks (MSc thesis), delivering 90%+ accuracy on complex, unstructured datasets.

● Directed the full lifecycle development of a .NET Core + Angular stock management system for enterprise operations, increasing inventory accuracy by 95% and cutting discrepancies by 60%.

● Enhanced enterprise system reliability and security for banking, telecom, and government infrastructures through advanced server hardening and proactive monitoring, achieving near-zero critical outages over a period of one year.