Apoorva
Verma
I ship full-stack products. Underneath them, the retrieval systems and evaluation harnesses that keep applied AI honest.
The chat below is one of them. Every answer is grounded in my real work before it reaches you.
Experience
Software Engineer · Oracle
2023 — nowBackend services and applied-AI work — RAG and LLM evaluation for enterprise search.
SWE Intern · Microsoft Engage
2022Built an ML-driven recommendation feature end-to-end.
SWE Intern · J.P. Morgan
2021Data tooling for an internal risk-analytics platform.
Fellow · MLH
2021Open-source fellowship — shipped to a production codebase.
Featured projects
rag-eval-harness
RAG over 15 arXiv papers with exact page and section citations. The real project is the eval harness: a reproducible retrieval and chunk×embedding comparison scored with DeepEval and a custom citation-faithfulness metric.
FounderSignal
Market-intelligence platform that mines and validates early startup signals. Full-stack Next.js app with data pipelines, scoring, and a dashboard.
promptguard
Regression testing for LLM apps: pytest for prompts. Write checks against prompt outputs so quality regressions fail in CI instead of reaching users.
langchain-shannonbase
A LangChain VectorStore for MySQL 9's native VECTOR type. Do RAG on ShannonBase, self-hosted MySQL, or HeatWave without standing up a separate vector database.