A driven Software & AI Engineer.
I build production data pipelines, ML systems, and backend tooling in Python & C/C++ — from AI-accelerator silicon at Intel to real-time race telemetry in motorsports.
Reliability and performance, carried from silicon to the racetrack.
I'm a Software & AI engineer with 3+ years building production data pipelines, ML systems, and backend tooling — grounded in the reliability and performance discipline of low-level hardware engineering.
At Intel I spent three years deep in AI-accelerator silicon, building C/C++ and Python tooling across 9+ FPGA and NPU programs, deploying Kubernetes-hosted dashboards, and automating the validation that kept engineering teams moving fast and correct under real load.
Today I design, train, and deploy ML models against real-time data. At FYM Technologies I architect end-to-end telemetry pipelines for professional racing teams — turning raw sensor streams into live strategy and performance predictions.
What I work with.
Experience & education.
Data & Systems Engineer
- Architect end-to-end pipelines that ingest, normalize, and stream live telemetry from Toyota GT4 and Ferrari / Porsche GT3 cars to engineers within seconds.
- Design and deploy ML regression models against live streams — real-time strategy and performance predictions, holding 98% reliability across a full race season.
FPGA Solutions Engineer
- Owned C/C++ and Python tooling across 9+ FPGA and NPU silicon programs — automated regression, system-level debug, and validation across firmware, drivers, and integration.
- Co-developed an FPGA transactor adding a new inter-IP layer for next-gen AI accelerators (+170% efficiency); built Kubernetes dashboards giving 3+ teams real-time visibility.
B.S. Computer Science
- Concentration in Artificial Intelligence, Minor in Management.
- Strong foundations in software engineering, databases, and algorithms.
Graduate Technical Intern
- Built a PostgreSQL telemetry pipeline and an interactive Grafana dashboard for FPGA build, run, and debug processes.
- Implemented a Keras regression model forecasting system behavior to optimize hardware workflows.
Data Analyst Intern
- Built ML models on emulation data delivering optimizations that reduced customer power usage by 45%.
- Developed a Python ETL pipeline over complex C++ data structures and designed the SQL schema.
Selected work.
CarManac — Global Vehicle Database
↗A production-grade, globally-standardized vehicle database on PostgreSQL + pgvector with per-fact provenance tracking — modeling a five-level hierarchy from make down to variant, plus a dedicated attribute table for long-tail specs.
Yellow-Flag Predictor
↗A Keras neural-net classifier generating live yellow-flag predictions at ~75% accuracy, fed by a Selenium pipeline scraping real-time IMSA timing into MongoDB. Integrating live APIs with WRL officials for redmist.racing.
FPGA Analytics Dashboard
INTEL · PRIVATEInteractive Grafana dashboards for Intel FPGA engineers, deployed via Kubernetes, with Keras regression models layering predictive analytics over live build, debug, and performance data for 3+ teams.
MotoData
↗A lightweight desktop viewer for motorsport data-logger files — fast, no-friction inspection of session data trackside.
VBOX Toolkit
↗A growing set of Python tools for working with VBOX files at the track — on its way to a packaged app aimed at replacing Circuit Tools.
NoCap
↗Backend for a service that suggests captions for uploaded images, powered by an ML model — front-end in progress.