Emirhan Ergül

As an AI Applications Solutions Engineer, I design and implement end-to-end AI architectures on the Azure ecosystem, specializing in LLM orchestration, multimodal inference, and scalable enterprise AI applications. I build production-ready RAG systems using GPT models, advanced vector and hybrid search architectures with Azure AI Search, and automated document processing flows integrated with SharePoint, Logic Apps, and Azure Functions. My engineering work spans developing high-performance backend services with FastAPI, designing secure REST APIs, and building user-facing applications with Streamlit and React. I leverage Microsoft Fabric for unified data access, semantic modeling, and analytics pipelines that support AI-driven workloads across large organizational data environments. I also architect multimodal AI solutions that combine image, text, and structured data processing, while implementing MLOps workflows with Azure Machine Learning and MLflow for model deployment, versioning, and operational monitoring. My role includes optimizing inference latency, embedding refresh cycles, token efficiency, and observability across AI applications, as well as designing agent-based workflows in Azure AI Studio and Copilot Studio. Through close collaboration with enterprise teams, I translate complex business and data requirements into reliable, scalable, and maintainable AI systems ready for real-world production environments.

As a Data & AI Specialist, I developed cloud-native AI and machine learning pipelines across the Azure ecosystem, integrating GPT models, NLP, Computer Vision, and Cognitive Services into enterprise workflows. I built machine learning pipelines on Azure Machine Learning—from data preprocessing and feature engineering to experiment tracking, deployment, and monitoring. My work included constructing data ingestion and transformation processes using Python, SQL, Docker, and event-driven cloud components, while also leveraging Microsoft Fabric for unified data modeling, analytics, and Lakehouse-based storage to support ML and AI workloads. I designed microservice-oriented AI components exposed through FastAPI-based REST APIs and contributed to applications built with Streamlit and React. By implementing MLOps practices such as model registry operations, continuous deployment, and performance monitoring, I delivered scalable, maintainable, and production-ready AI solutions aligned with enterprise needs.