Lead Data Scientist building production agentic AI systems
I build AI systems and production products that solve real problems. Over the past 6+ years, I've worked across the full spectrum—from designing intelligent agents that automate complex workflows, to developing high-performance time-series infrastructure, to creating tools that help people make better decisions with data.
My work sits at the intersection of research and engineering. I take emerging techniques in AI and machine learning and turn them into reliable, scalable systems that deliver value. The common thread is building production-grade software that people actually use and trust.
Design and deploy multi-agent platforms using LangGraph, LangSmith, and RAG. Built enterprise systems handling $80B+ in annual business processes with 20+ FTEs freed.
Deploy ML systems on Kubernetes that actually scale. Rewrote critical forecasting libraries in Rust for 10x performance improvements in production pipelines.
Build conversational analytics agents that let business users query complex data warehouses in natural language and get context-aware insights.
Set up CI/CD pipelines, Kubernetes deployments, and observability frameworks for production AI systems on GCP and AWS.
Build costing engines, pricing algorithms, and predictive models that form the backbone of business strategy and drive real ROI.
Create "trust but verify" frameworks for production AI, handling model validation, observability, and responsible deployment of business-critical systems.
High-performance time-series forecasting library in Rust. Prophet reimplementation running in production for mission-critical decision systems.
Exploring the challenges and interesting discoveries from reimplementing Facebook's Prophet forecasting library in Rust.
Read More →Interested in agentic AI, production ML systems, or building AI that drives real business value? Let's connect on LinkedIn or check out my code on GitHub.