I build production AI agents end-to-end — controlled LLM orchestration runtimes, retrieval systems, media pipelines, and inference-aware deployments.
I'm an AI Engineer focused on agentic systems, retrieval, and LLM inference. At Spice (thespice.ai) I built the company's AI products and agents from the ground up — including Pulse / Lucid, a creator "content chief-of-staff" agent harness (try-pulse.ai), and AskSpice, conversational search over 10,000+ podcast episodes and 1M+ searchable chunks.
My strongest work spans agent-harness engineering, LLM orchestration, tool execution, Retrieval-Augmented Generation, hybrid search, durable memory, automated decision loops, and LLM inference engineering.
🎓 M.S. Data Analytics — University of Illinois Springfield (4.0 GPA) 📍 Austin, TX · 💬 open to AI / LLM / agent engineering roles
| Project | What it does | Stack |
|---|---|---|
| EchoFind | Memory-aware conversational RAG engine that returns the single best podcast clip or episode for a natural-language question | FastAPI · Gemini · Pinecone · Cohere · Postgres |
| Clip'O'pedia | Mention-driven hybrid-RAG clip recommender on a LangGraph pipeline — runs fully offline with zero API keys | LangGraph · HyDE · RRF · hexagonal |
| ReelForge | AI post-production pipeline: turns a talking-head clip + transcript into a polished 9:16 captioned reel | Gemini · FFmpeg · MediaPipe · MoviePy |
| Artha Council | Staged, multi-model AI investment committee with a fail-closed OpenClaw agentic broker bridge | GPT-5.5 · Gemini · Claude · MCP |
| Commitment Decay Engine | Turns meeting transcripts into a commitment ledger and reconciles follow-through | Python · CLI · LLM-ready |
New here? Start with EchoFind (memory-aware RAG) or Artha Council (multi-model agents + fail-closed agentic execution).
Languages
AI · Agents · Retrieval
Backend · Infrastructure
LLM Inference
