IQVIA

Spearheaded development of enterprise-grade microservices architecture using .NET Core 8, implementing Clean Architecture, CQRS with MediatR, and Domain-Driven Design principles to deliver scalable healthcare solutions supporting millions of patient records

Engineered AI-powered clinical decision support system leveraging OpenAI GPT-4 and Azure OpenAI Services, enabling healthcare professionals to access evidence-based treatment recommendations and drug interaction warnings

Delivered 40% reduction in clinician research time by implementing sophisticated RAG system with Pinecone vector databases, enabling AI-driven medical literature search across millions of research papers

Optimised data warehousing infrastructure by integrating Snowflake OLAP capabilities, achieving 7% improvement in processing time for large-scale clinical datasets whilst reducing AWS operational costs

Built robust offline-capable iOS mobile solution using DuckDB and SQLite, enabling field researchers to access and synchronise critical patient data in areas with limited connectivity

Architected secure RESTful APIs using ASP.NET Core Web API with dependency injection, implementing repository and unit of work patterns for maintainable, testable code

Improved clinical trial data accuracy by 35% through development of intelligent ML-based quality monitoring system that automatically detects anomalies and data inconsistencies

Established modern DevOps practices by deploying containerised .NET applications with Docker and Kubernetes on AWS EKS, implementing comprehensive CI/CD pipelines through GitHub Actions