I build production-grade AI systems for enterprise workflows, document intelligence, retrieval-augmented generation, and automation.
I am an AI/ML Engineer specializing in Generative AI, Agentic Workflows, and Retrieval-Augmented Generation (RAG) systems.
My work focuses on building scalable AI applications that combine LLMs, knowledge graphs, vector search, OCR, structured validation, and cloud-native deployment. I enjoy turning complex unstructured documents into searchable, auditable, and production-ready AI systems.
Currently, I work on enterprise AI systems involving:
- Graph RAG across large-scale PDFs and image-based documents
- OCR-driven document intelligence pipelines
- LangChain-based AI orchestration workflows
- Neo4j knowledge graphs and PostgreSQL metadata indexing
- Schema-constrained LLM outputs using Pydantic and JSONSchema
- Secure AI workflows with audit logging, validation, and CI/CD deployment
Built an enterprise Graph RAG system for large-scale PDFs and image-based documents using OCR, hierarchy-aware chunking, embeddings, Neo4j knowledge graphs, LangChain orchestration, and PostgreSQL metadata indexing.
Core capabilities:
- Hybrid retrieval using graph traversal, vector embeddings, and BM25 ranking
- Parent-child and sibling document relationships for contextual reasoning
- Cross-document retrieval over deeply nested enterprise content
- Schema-constrained LLM outputs using Pydantic and JSONSchema
- Audit-ready validation, logging, and deterministic output enforcement
Developed an OCR-driven AI workflow platform using Azure AI Foundry OCR, FastAPI, React, SQL Server, and XML-based integrations.
Core capabilities:
- Automated document processing across PDFs, images, and email attachments
- OCR extraction and validation pipelines
- Role-based access control
- API submission workflows
- Audit logging for secure enterprise AI operations
- Markdown and machine-readable output generation for downstream AI workflows
Designed LLM-driven automation workflows for lead qualification, campaign routing, decision logic, and business-rule execution.
Core capabilities:
- Multi-step autonomous workflow orchestration
- Prospect classification and redistribution
- Conditional routing across enterprise workflow systems
- Reduced manual intervention through AI-driven automation
- Production-grade RAG evaluation
- Graph-based retrieval optimization
- Agentic AI workflow design
- OCR + LLM document transformation
- Secure AI systems for regulated environments
- Cloud-native AI deployment patterns
