Rochester, Minnesota
I am a software engineer at Mayo Clinic IT focused on machine learning infrastructure and internal platforms. I build GPU-ready compute, distributed execution environments, and cloud-native services that help teams train and deploy ML workloads reliably.
My work spans Terraform-based automation on GCP, SLURM-based GPU clusters, observability for ML systems, and secure API services designed for governance and auditability. I enjoy building platform capabilities that improve developer velocity while raising the reliability and safety bar by default.
Software Engineer, Generative AI Program · Mayo Clinic IT
M.Sc. Biomedical Engineering and Physiology · Mayo Clinic Graduate School
B.Sc. Industrial Engineering and Management · Ben Gurion University
The production capabilities I’ve designed, deployed, and operated.
This section focuses on outcomes and responsibilities rather than tools.
Designed and operated internal services for model/provider access with strong access controls, secret management, audit logging, and automated credential rotation — enabling self-service use without losing governance.
Built GPU-ready environments with SLURM and GCP Managed Instance Groups, including MIG-based partitioning and scalable patterns for shared workloads, experiments, and distributed execution.
Developed reusable Terraform + Ansible modules and CI/CD patterns that make platform delivery repeatable, reviewable, and easier to operate over time.
Integrated GPU telemetry and health signals (NVIDIA DCGM + Cloud Monitoring) to support SLO-driven operations, faster debugging, and better capacity planning for ML workloads.
Strong defaults, automation, and observability reduce friction without sacrificing reliability or trust. When platforms provide clear guardrails and visibility, engineers spend less time fighting infrastructure and more time shipping.
That approach scales — in big-tech environments and regulated domains — because it is fundamentally about operational clarity.
The core technologies and systems I work with across ML infrastructure, distributed compute, and cloud engineering.
Mayo Clinic · Generative Artificial Intelligence Program
Remote · Jan 2024 to PresentI build and operate ML infrastructure for the Generative AI Program, including GPU compute environments, SLURM automation, and distributed training support on GCP. I developed a GPU observability stack using Cloud Ops and NVIDIA DCGM to improve reliability and diagnostics, and maintain internal FastAPI services for secure model access and credential management integrated with GCP Secret Manager and infrastructure-as-code workflows using Terraform and Ansible.
Mayo Clinic · Multimodal Neuroimaging Laboratory
On-site · May 2021 to Jan 2024I built high-throughput pipelines for diffusion MRI and intracranial EEG processing using Python, Linux, and distributed workflows. I led development of HED SCORE, an open source EEG metadata framework adopted by international research teams, and designed tools for multimodal data integration, structured metadata management, and quality control across large neuroimaging datasets in collaboration with clinicians and data scientists.
Mayo Clinic · Bioelectronics Neurophysiology and Engineering Laboratory
On-site · May 2018 to May 2021I worked on ML for long duration EEG and wearable sensor data, developing LSTM-based seizure prediction models, integrating real time ML components into clinical grade monitoring systems, and building ingestion, preprocessing, and feature extraction pipelines for large biosignal datasets in collaboration with neurology and engineering teams.
Mayo Clinic · Bioelectronics Neurophysiology and Engineering Laboratory
On-site · May 2016 to May 2018I prototyped closed loop neurostimulation systems that combined hardware signals with real time software control and engineered backend tools for structured patient data tracking, automation, and biosignal analysis while supporting research teams with software development, data workflows, and database design for experimental studies.
Internal service for managing API keys and access to language model providers. Integrates with GCP Secret Manager, includes audit logging and scheduled rotation, and provides a simple admin UI for platform and security teams.
Unified GPU telemetry stack that collects metrics and health signals from GPU nodes and exposes them in dashboards and alerts. Enables teams to track utilization, debug issues, and plan capacity for ML workloads.
Built an automated system for creating and managing SLURM clusters on demand for research teams, using instance templates and infrastructure automation to standardize configuration and simplify lifecycle operations.
Machine readable metadata framework for EEG annotations that supports FAIR aligned data sharing and large scale analysis. Designed to make neurophysiology datasets easier to reuse across labs and tools.
View on Google ScholarPython based pipelines for diffusion MRI and intracranial EEG preprocessing and analysis. Built to support reproducible studies and collaboration between engineering and clinical research teams.
Development of models and algorithms for long-duration EEG and wearable sensor data, including LSTM-based seizure prediction, anomaly detection, forecasting of biomedical time series, and signal artifact reduction.
View on Google ScholarSystem for seizure risk forecasting using EEG and wearable data. Combines feature engineering, temporal modeling, and long horizon prediction for real world monitoring scenarios.
View on Google ScholarConnect with me to discuss roles or collaborations related to ML infrastructure, platforms, and cloud-native ML systems.
Open LinkedIn Profile class="links-card" ResearchBrowse my publications and patent work related to seizure prediction, EEG metadata, and multimodal neuroimaging.
View Google ScholarIf you’re building ML infrastructure, platform tooling, or secure AI services — I’d love to connect.
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