Available for opportunities · Cincinnati, OH
AI Scientist & GenAI Architect · RAG & LLM Systems in production at P&G
01 — About
I'm an AI Scientist — RAG & LLM Systems at Procter & Gamble's Digital Accelerator, where I architect production-grade agentic systems handling real enterprise workloads.
Currently completing my M.Eng. in Computer Science at the University of Cincinnati (April 2026), I specialize in multi-agent orchestration, hybrid retrieval pipelines, and making LLMs actually reliable in enterprise environments — with full governance, observability, and responsible AI compliance.
I shipped StatVisor — a multi-agent AI platform with BM25 sparse + dense hybrid retrieval, 95%+ accuracy, and 40% reduced API downtime. And I built Mockview, an AI mock interview platform, because I couldn't stop thinking about the problem.
02 — Skills
From neural architecture to production APIs — the technologies I reach for when something needs to actually work.
03 — Projects
Production systems and side projects — shipped, not just started.
Multi-agent AI platform at P&G's Digital Accelerator. BM25 sparse + dense hybrid retrieval with OCR-based document ingestion; 95%+ retrieval accuracy across large enterprise datasets. Full LLM governance with audit logging, cost controls, schema validation, and real-time Grafana + Chainlit observability dashboards — reduced API downtime by 40%.
Full-stack AI mock interview platform. Gemini AI adaptive question generation across 8+ categories with automated scoring on 5 dimensions. JWT auth with FastAPI/Supabase backend; trimmed API response times by 210ms and DB query times by 75%. Deployed via CI/CD on Render and Vercel with <2s load times.
LLM memory optimization research project. Designed a memory management layer with context compression, FAISS-backed semantic prioritization, and fine-tuning strategies to reduce token usage while preserving reasoning quality. Async benchmarking engine across 10+ model configs on latency, memory, and accuracy.
Real-time facial emotion classification using deep CNNs. TensorFlow/Keras model with OpenCV integration. Explored transfer learning and custom architecture design for affective computing.
Built from scratch — neural network canvas with Three.js, custom cursor, scroll-triggered animations, and an AI chatbot. Zero frameworks, pure HTML/CSS/JS.
04 — Experience
05 — Credentials
Open to full-time AI/ML engineering roles & agentic system projects.
● AI-powered · knows everything about me