AI
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Architected and executed GenAI Proof-of-Concepts (PoCs) leveraging LangChain and LangGraph to build multi-step, stateful document processing pipelines that automated unstructured data extraction from high-volume business PDFs.
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Architected GenAI Proof-of-Concepts (PoCs) and intelligent document processing pipelines using LangChain and LangGraph, automating unstructured data extraction and stateful workflow routing for high-volume business PDFs
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Architected GenAI Proof-of-Concepts (PoCs) utilizing LangChain and LangGraph to build stateful document processing pipelines, automating unstructured data extraction and workflow routing for high-volume PDFs
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Designed hybrid search architectures combining keyword indexing and Vector Databases (e.g., pgvector, Chroma) to deliver semantic document search capabilities directly integrated into AWS S3 file ingestion workflows.
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Designed and integrated hybrid search architectures combining keyword indexing and Vector Databases (pgvector) to deliver low-latency semantic document search capabilities within AWS S3 file ingestion workflows.
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Designed hybrid search architectures combining keyword indexing and Vector Databases (pgvector) to integrate semantic document search directly into AWS S3 and Spring Boot ingestion workflows
- Engineered polyglot cloud-native microservices integrating Spring Boot Java backends with asynchronous Python serverless workers (AWS Lambda and Step Functions) for secure, scalable AI payload orchestration.