AI Software Engineer building production multi-agent and RAG systems in Python and TypeScript, all
deployed and live on Railway and Vercel. Systems ground their outputs in retrieved evidence with
citations, score confidence from observable signals, hand off to a human when they should, and are
gated by evaluation suites in CI. Works spec-first with AI-assisted development (Claude Code and other
agentic coding tools), pairing velocity with strict typing and 1,800+ automated tests.
Projects
- Multi-agent memo pipeline shipped as the production default only after a paid LLM-as-judge evaluation showed it beat the single-pass baseline (groundedness 97.9% vs 95.9%, citations 100% vs 97.7%).
- A deterministic scope gate decides which laws apply, so the compliance verdict cannot be hallucinated; the model writes only cited explanation prose. Grounded RAG over 12 AI-regulation laws across 7 jurisdictions, each in its own operative legal test; adding a law is a data change, not code.
- Same engine exposed as an MCP server (five read-only tools); a monitoring agent opens pull requests as the human gate. 357 tests, CI.
- Python, FastAPI, Streamlit, Chroma, fastembed (BGE-small), Claude Haiku + Sonnet + Opus, GitHub Actions CI, Railway + Vercel
- Multi-tenant RAG: each user's GEDCOM tree embeds into a private Pinecone namespace, retrieved with a public emigration corpus; a three-node LangGraph pipeline hands off to the user when retrieval confidence is low rather than inventing ancestry.
- Python FastAPI evaluation microservice scores every narrative on six metrics (Ragas, LLM-as-judge), regression-tested in CI with promptfoo and traced in LangSmith.
- TypeScript, LangGraph, Pinecone, OpenAI GPT-4o + Whisper, ElevenLabs, Python FastAPI, React 19
- 12 scout agents poll nine government data sources 24/7; a five-agent swarm on a Redis Streams pipeline correlates events against 750 IUCN species ranges in PostGIS and posts Discord alerts.
- Self-improving Refiner/Evaluator loop scored real flood and coral-bleaching predictions against observed outcomes and rewrote its own agent prompts in production. 470 unit tests + 43 E2E.
- TypeScript, Node.js, Express 5, Redis Streams, PostGIS + pgvector, Gemini, Claude, Next.js 15, Railway
- Four-agent swarm plus orchestrator analyzes 35,000+ near-Earth objects; each agent calls live NASA APIs via Anthropic tool use, with dual RAG indices and per-event SSE streaming of every tool call.
- Confidence computed from observable fields, never self-reported; human-handoff threshold calibrated empirically. 209 server tests (97% coverage) + 226 E2E.
- TypeScript, Angular 21, Express 5, Supabase + pgvector, Claude Sonnet, Voyage AI, Three.js
Also live: Poster Pilot (multimodal RAG over 5,000+ historical posters, CLIP + Reciprocal Rank Fusion),
SoilProve (fertilizer-prescription tool, multi-provider LLM failover, built solo in 5 days), and
ChronoQuizzr (geography trivia, adversarial two-agent clue-verification pipeline).
Experience
AI Software Engineer · Self-directed · Remote · January 2026 – present
Designed, built, and operate the production AI systems above: agent orchestration, grounded RAG and
vector search, evaluation pipelines (golden datasets, LLM-as-judge), streaming APIs, an MCP server, and
cost-aware multi-model routing, in Python and TypeScript.
Student Software Engineer (Apprenticeship) · Codefi · Remote · January 2025 – present
Year-long full-stack bootcamp (JavaScript, TypeScript, Angular, React, Ruby on Rails), followed
by Codefi's AI Skills track: Prompt-to-Product and the production AI engineering Masterclass.
Education
- Codefi — AI Engineering & Skills Training: Prompt-to-Product & AI Masterclass (2026)
- Codefi CodeLabs — Full-Stack Web Development Bootcamp (2025)
- SMU Dedman School of Law — J.D.
- Indiana University Bloomington — B.A.
Skills
AI / LLM: Retrieval-Augmented Generation (RAG), multi-agent orchestration, LLM-as-judge evaluation, golden datasets, Model Context Protocol (MCP), LLM APIs (Anthropic, OpenAI, Google), LangGraph, vector databases (Chroma, Pinecone, pgvector), prompt engineering, spend-safety
Languages & backend: Python (FastAPI, Streamlit), TypeScript (strict mode), Node.js, Express 5, Ruby on Rails, PostgreSQL, PostGIS, Redis Streams, SSE streaming
Frontend: React, Angular, Next.js, Tailwind CSS
Testing & delivery: pytest, Vitest, Playwright, GitHub Actions CI, Docker, Railway, Vercel