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 <2s latency.
• 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.