My Resume

Moises Aguirre

Seattle, Washington, United States | aguirremoy@gmail.com | 626-427-4457 | LinkedIn | GitHub

Google (May 2026 - Present) | Kirkland, WA
- Engineered distributed control plane APIs in Go for Google Cloud’s managed Lustre file system to support high-throughput AI/ML workloads.
- Optimized communication workflows using gRPC, reducing cluster deployment latency by 40% and enabling up to 10 TiB/s aggregate performance.
- Architected dynamic IAM-based mount authorization within the Lustre control plane to enforce fine-grained identity permissions.

Microsoft (September 2024 - May 2026) | Redmond, WA
- Spearheaded the ARM64 migration for critical Microsoft Defender for Endpoint services, modernizing application architecture and containerizing workloads.
- Delivered a 30% reduction in memory usage and a 15% reduction in CPU consumption, directly lowering COGS.
- Replatformed a mission-critical signature update orchestration service from Service Fabric to Kubernetes ARM clusters, cutting infrastructure costs by 31%.
- Resolved complex ARM64 performance bottlenecks for Azure Linux 3 and contributed technical fixes to public Microsoft repositories.

Capital One (June 2022 - September 2024) | McLean, Virginia
- Optimized Amazon Macie architecture and integrated stakeholder APIs to automate cyber incident reporting, reducing monthly operational costs by $14,000.
- Engineered an automated false alert analysis system for Amazon Macie, cutting cybersecurity investigation time by 70%.
- Directed a usage standardization campaign that drove the adoption of scalable serverless architectures across product teams.

Naval Surface Warfare Center (February 2021 - May 2022) | Corona, CA
- Architected Python Flask web applications for data analysts, reducing technical report generation time by 85%.
- Developed time-series analysis tools for AEGIS Weapon Systems, improving the accuracy of key performance and lifecycle data points.
- Automated system operational performance reporting, saving approximately 120 billed hours per detected anomaly event.

Naval Surface Warfare Center (May 2018 - February 2021) | Corona, CA
- Engineered workflow automation frameworks to streamline data processing and enable analysts to extract key data points efficiently.
- Re-engineered Fire Control System data message processing, resulting in a 90% reduction in processing time.
- Provided critical on-ship reconstruction and SIMDIS replays for Tactical Instructors during high-stakes SWATT exercises.

University of California, Riverside
Riverside, CA • 2017

Languages: Go, Python, C#, C++, JavaScript, TypeScript, SQL
Cloud Platforms: GCP (IAM, Compute Engine, Lustre, Storage), AWS (API Gateway, CloudFormation, EC2, EventBridge, DynamoDB, Lambda, Route 53, S3, SQS, Step Functions), Azure (Azure Kubernetes Service / AKS, Blob Storage, Azure DevOps Pipelines, Virtual Machines)
DevOps & Observability: Kubernetes, Docker, Terraform, Azure DevOps CI/CD Pipelines, Jenkins, Prometheus, OpenTelemetry, Performance Profiling Tools, UNIX
Architecture & Systems: Distributed Systems, gRPC, Lustre Parallel File System, Serverless Architectures, ARM Architecture
Certifications: CompTIA Security+ CE