I build high-volume production systems in C#, React, and SQL Server. Lately, I've been wiring those same skills into LLMs, RAG pipelines, and multi-agent workflows, all running on local GPU hardware.
For the past 7+ years, I've been building the kind of software that can't go down: C# APIs, SQL Server architectures, React frontends, all running at scale for companies that process serious volume. One system I optimized handled $1.5 Trillion in historical insurance data. Another tool I built cut a team's monthly support tickets by 90%.
I've worked across healthcare platforms for seniors, a workforce tracking system serving 11,000 daily users at the world's largest BMW plant, and AI-driven injury evaluation services used by 18 of the top 20 US insurance carriers.
Now I'm taking all of that production discipline and applying it to AI. I'm building RAG apps, experimenting with multi-agent systems, fine-tuning open models on my own NVIDIA DGX Spark, and stacking certifications that actually reflect hands-on skill.
Production .NET and data engineering at the core, with an expanding AI and ML toolkit on top.
Insurance, healthcare, automotive, real estate. Different industries, same bar: the system has to work at scale and it can't break.
Not collecting badges. Picking certifications that prove I can build, not just define terms.
Microsoft Certified — covers AI concepts, Azure ML, computer vision, and NLP services
Building and deploying Azure Cognitive Services, AI solutions, and knowledge mining pipelines
Training, evaluating, and deploying ML models with Azure Machine Learning
Cross-cloud ML chops: data engineering, modeling, and deployment on AWS
Each one solves a real problem. No toy demos, no tutorial clones.
Ask plain-English questions against technical docs and get grounded answers back. C# backend handles the orchestration, React frontend keeps it clean, and embeddings run locally on DGX Spark so nothing leaves the machine.
A multi-agent framework where a planner, coder, and reviewer work together to break down complex tasks. LangGraph handles the workflow graph, .NET ties it all together, and the agents actually argue with each other before shipping an answer.
A Model Context Protocol server that gives LLM-powered apps safe, structured access to SQL Server databases and internal tools. Think of it as a permissions layer between your AI agent and your production data.
Benchmarking suite for local LLM inference on the DGX Spark. Measures throughput, latency, and memory across different quantization levels and model families so I know exactly what this hardware can handle.
Production-grade AI workloads on personal hardware. No cloud bills, no rate limits, no waiting.
Running models on my own hardware means I can experiment without watching API costs climb, keep data completely private, and iterate on model behavior without hitting rate limits or usage caps. When you own the compute, you move faster.
The DGX Spark's unified memory lets me load models that would normally require multiple consumer GPUs. That means real fine-tuning and proper benchmarking, not just running demos.