Bridging Data Infrastructure
and Production AI.

Technical Background

My background is in heavy-lifting Data Engineering. At EverCommerce and Statistics Canada, I architected data lakes, migrated legacy systems to the cloud, and managed orchestration for mission-critical pipelines. I learned that reliable AI is impossible without reliable data infrastructure.

The Pivot to AI Engineering

I founded Volkdata to apply my infrastructure engineering rigor to the ML stack. Rather than just tuning models, I focus on the systems that make them viable in production.

• Inference Optimization: Solving the "Memory Wall" by benchmarking PagedAttention (vLLM) and implementing quantization for edge devices.
• System Reliability: Engineering shadow deployment layers and A/B testing frameworks to validate models without risking user experience.
• Data-Centric AI: Building automated data mining loops to fix model blind spots (like detecting hard negatives in computer vision) systematically.

I am looking to join a team where I can apply this end-to-end perspective—building the platform that allows research to scale into production.