Artificial Intelligence Engineer

Professional Summary Hands-on GenAI Engineer with 9+ years of experience building production-grade RAG systems in Azure. Designed scalable ingestion pipelines for 10K+ enterprise PDFs, implemented metadata-driven access control using Unity Catalog and Azure AD, and deployed GPT-powered APIs serving 2,000+ daily users. Strong expertise in Azure OpenAI, Databricks, vector search, and secure API architecture. Key Production Experience Enterprise RAG Platform – Healthcare Client Designed ingestion pipeline for 12,000+ clinical documents using Azure Document Intelligence. Implemented structure-aware chunking with table handling. Built embedding pipeline using Azure OpenAI and Azure AI Search. Implemented department-level filtering via metadata + Azure AD claims. Reduced hallucination rate by 32% using retrieval evaluation and prompt enforcement. Deployed FastAPI service with Azure API Management and OAuth2 authentication. Processed 5,000 queries/day with Implemented token cost monitoring and caching strategy reducing cost by 28%. Databricks-Based RAG Migration – Financial Client Built Bronze/Silver/Gold Delta architecture for document lifecycle. Used Unity Catalog for row-level security. Managed notebook CI/CD using GitLab. Implemented incremental embedding for versioned documents. Technical Skills Azure OpenAI, Azure AI Search, Azure Functions Databricks, Unity Catalog, PySpark FastAPI, .NET API, OAuth2, Azure AD LangChain (used sparingly in production) Redis vector search MLflow, CI/CD (GitHub Actions) Monitoring via Azure Monitor & Prometheus Metrics Section (Very Important) Strong CVs include: Number of documents handled Daily query volume Latency improvements Cost reduction % Evaluation score improvements Uptime % Weak CVs avoid metrics.