AI @ Lilly · building agentic AI & LLM systems that ship.
I work at the intersection of applied AI and real-world deployment — turning research-grade ideas into systems that run in production. My background runs the full stack: from implementing neural networks and decision trees from scratch, to designing multi-agent LLM systems and standing up the cloud + CI/CD plumbing that keeps them alive.
- Currently focused on multi-agent systems, GraphRAG/RAG pipelines, and LLM application engineering
- I like understanding things at the implementation level — from BSTs and tail recursion up to RLHF and model evaluation
- Equally at home in a Python notebook, a Rust toolchain, or a Raspberry Pi
- Fun fact: I've taught a Duckietown robot to drive itself and built ML models from scratch before reaching for a framework
| Area | Tools & techniques |
|---|---|
| Agentic AI / LLM systems | Multi-agent design · GraphRAG / RAG pipelines · LLM app engineering · GPT-4o · ElevenLabs (TTS) · Whisper (STT) · Cortex · n8n / Langflow |
| AI / ML | Neural networks · decision trees · computer vision · data mining · NLP / sentiment analysis · prompt engineering · RLHF & model evaluation |
| Cloud & data engineering | GCP (Pub/Sub, Cloud Scheduler) · Microsoft Fabric · real-time data pipelines · Denodo |
| Deployment / DevOps | GitHub Actions (CI/CD) · OIDC auth · Artifactory · Docker · CATs · Git · ROS |
| Finance & BI | Power BI · FactSet · PitchBook · net revenue / variance decomposition |
| CRM & automation | GoHighLevel · Twilio · Airtable · Notion · Calendly · Mailchimp / Brevo |
| Embedded & robotics | Raspberry Pi · Pi Pico · microcontrollers · Duckietown self-driving |
| CS foundations | Data structures & algorithms (TA'd ECE 368) · discrete math · sorting · BSTs · tail recursion |
