01 / Current project
GenAI Bot
June 2026 – Present
A serverless AWS pipeline for answering financial questions over SEC EDGAR data, backed by Claude Sonnet via Amazon Bedrock.
- Architected a serverless AWS pipeline with SAM, Lambda, S3, and EventBridge: deploying a scheduled EDGAR data refresh function and an on-demand document retrieval function backed by Claude Sonnet via Amazon Bedrock.
- Designed Lambda I/O contract with input validation, cross-region Bedrock inference, and structured JSON responses including model metadata and latency tracking.
- Configured Python 3.12 runtime with explicit dependency packaging and IAM-scoped policies, ensuring least-privilege access across S3 and Bedrock resources.
ServerlessAWSGenAI
View GenAI Bot on GitHub →
☁️AWS SAM
⚡Lambda
🪣S3
🔀EventBridge
🐍Python 3.12
✨Claude / Bedrock
02 / Coursework project · ACM@JHU Coding Circles
LaunchStack
Spring 2026
Reverse-engineered and documented a production RAG ingestion pipeline, mapping a 7-stage durable workflow across checkpointed, retry-safe background jobs.
- Reverse-engineered and documented a production RAG ingestion pipeline (Next.js, Postgres/pgvector, Inngest, OpenAI embeddings), mapping a 7-stage durable workflow — route, OCR, chunk, embed, store, finalize, graph enrichment — across checkpointed, retry-safe background jobs.
- Built a failure-mode matrix across OCR routing, normalization, and chunking stages, identifying silent failure surfaces (e.g. unhandled exceptions leaving jobs stuck in a "queued" state, silent fallbacks masking degraded routing) and flagging idempotency risks in non-atomic database writes.
- Diagnosed a status-tracking gap where job state conflated "searchable" with "fully enriched," and proposed an explicit status enum to give downstream teams accurate visibility into pipeline health.
RAGPipeline designSystems analysis
View LaunchStack on GitHub →
▲Next.js
🐘Postgres
🧭pgvector
🤖OpenAI embeddings
⚙️Inngest
