Hi, I am Gaurav Deshmukh,
A driven Data & Software Engineer.
I specialize in Python & C++ development, AI, data analytics, and software support for semiconductor design—from FPGA solutions at Intel to trackside telemetry in motorsports.
About Me
I am a driven Software Engineer specializing in scalable backend infrastructure, machine learning pipelines, and AI-driven analytics.
In my most recent role at Intel, I spent three years developing robust automation scripts and deploying Kubernetes-hosted systems that significantly reduced manual validation overhead for hybrid FPGA solutions. I am passionate about bridging the gap between raw data and actionable decision-making.
I am always looking for ways to push the boundaries of technology, which led me to combine my expertise in machine learning with my love for motorsports. I currently develop predictive machine learning models to analyze real-time race telemetry, optimizing track strategy and driver performance for professional racing teams.
I am a driven Software Engineer specializing in scalable backend infrastructure and AI-driven analytics. At Intel, I deployed Kubernetes systems that reduced validation overhead for hybrid FPGA solutions.
Combining my ML expertise with motorsports, I currently develop predictive models to analyze race telemetry and optimize track strategy for professional racing teams.
Tech Skills
Interests
Experience & Education
Data Analyst Intern
Siemens EDA
- Engineered predictive analytics and machine learning models utilizing emulation data, delivering optimization insights that reduced customer power usage by 45%.
- Developed a Python-based ETL pipeline to efficiently extract, process, and consume complex C++ data structures.
- Designed and managed SQL databases to structure, merge, and organize full-scope raw emulation data for downstream analysis.
- Engineered predictive ML models, providing insights that reduced customer power usage by 45%.
- Developed Python ETL pipelines and SQL databases to structure emulation data.
Graduate Technical Intern
Intel Corporation
- Architected an automated PostgreSQL data pipeline, extracting Intel log telemetry and securely routing 100+ daily records for downstream analytics.
- Designed and deployed an interactive Grafana dashboard to visualize FPGA build, run, and debug processes, reducing manual analysis time by 160%.
- Implemented a Keras deep-learning regression model for predictive analytics, forecasting system behavior to optimize future hardware workflows.
- Architected automated PostgreSQL log telemetry pipelines.
- Deployed Grafana dashboards to visualize build status, reducing manual analysis time by 160%.
B.S. in Computer Science
UC Irvine
- Concentration in Artificial Intelligence
- Minor in Management.
- Developed strong foundations in software engineering, databases, and algorithms.
FPGA Solutions Engineer
Intel Corporation
- Engineered comprehensive software tools for hybrid FPGA client/server verification workflows, accelerating validation across 9+ silicon and IP programs.
- Co-developed a novel NPU transactor for next-gen architectures, optimizing chip-level data transactions to increase system efficiency by 170%.
- Deployed Kubernetes-hosted dashboarding systems, providing full-scope build, debug, and performance tracking for 3+ engineering teams.
- Developed Python and SQL automation scripts for log ingestion, eliminating 10 hours of manual validation overhead per week.
- Engineered software tools for hybrid FPGA client/server workflows.
- Co-developed novel NPU transactors to increase efficiency by 170%.
- Deployed Kubernetes-hosted dashboards and Python/SQL automation scripts.
Data & Systems Engineer
FYM Technologies
- Manage trackside data and electrical systems for 3 race teams across Toyota GT4, Ferrari Challenge/GT3, and Porsche Cup platforms.
- Analyze real-time telemetry utilizing WinTax and VBOX during race sessions, optimizing driver and vehicle performance by 30%.
- Direct ECU integration and troubleshoot complex electrical systems, ensuring 98% reliability for Teradek live video and data streams.
- Develop ML regression models to translate raw race telemetry into actionable track strategy and driver insights.
- Manage trackside data and ECU integrations for 3 race teams across GT4, GT3, and Porsche Cup.
- Analyze WinTax/VBOX telemetry, optimizing track performance by 30%.
- Develop ML models translating telemetry into strategic driver insights.
Featured Projects
AI Yellow Flag Predictor
Engineered a neural net binary classifier using Keras to generate live yellow flag predictions with 75% accuracy for IMSA events. Developed a Selenium data pipeline to scrape live timing data and store telemetry in MongoDB.
FPGA Analytics Dashboard
Designed an interactive Grafana dashboard ecosystems for Intel FPGA Engineers, deployed via Kubernetes. Implemented predictive analytics through Keras regression models.