Hi, my name is
Arbab Chowdhury
AI & Data Engineer for regulated financial systems.
I build regulatory-grade data platforms and production RAG / GenAI systems for banks and compliance-heavy teams — with the correctness, auditability, and rigor that regulated data demands.
Résumé Get in touch01. About
Background
I'm a senior data engineer and software architect at PNC, a top-10 U.S. bank, where I've spent 15+ years making sure financial and regulatory data is correct, compliant, and on time. I designed a relational data model that has run in production for over a decade without a failure, and I validate Basel III / LCR liquidity calculations against source-of-truth reconciliations in OFSAA.
Now I build the AI layer on that same rigor: retrieval-augmented generation over regulated documents, AWS Bedrock pipelines, and agentic workflows — with the citation discipline, evaluation harnesses, and audit trails that regulated data requires.
I bring enterprise depth to AI work and AI leverage to enterprise work, and I make pragmatic build-vs-buy calls across AWS, Azure, and GCP.
02. Work
Selected projects
RegIntel — Regulatory Intelligence Platform flagship
Citation-grounded RAG assistant over banking regulation (Basel III/LCR, FFIEC, OCC). AWS Bedrock-native — Titan embeddings, Claude Converse, hybrid retrieval, refusal-aware eval harness.
RAG, three ways flagship
One document-Q&A use case shipped three ways — no-code (n8n + Supabase), managed cloud (Bedrock Knowledge Base), and custom (Python RAG) — with an honest cost / latency / control trade-off table. How I make build-vs-buy calls, in code.
ComplianceIQ
The same retrieval architecture applied to a second regulated domain. Hybrid (dense + BM25) retrieval with reciprocal-rank fusion, citation enforcement, and Claude-as-judge faithfulness evaluation.
More on github.com/arbabc-ai.
03. Writing
Notes & teardowns
Coming soon — architecture notes, build-vs-buy decisions, and RAG-evaluation writeups from regulated-data work.