ian
computer science student @ fiu
incoming fall intern @ google svl, working on gcloud apps
i'm currently interning at , working on network infrastructure for large-scale datacenters, building sustainable platforms that keep capacity ahead of demand
previously at amazon (fall 2025), where i built llm-powered diagnostic systems that tie together aws bedrock with real-time data from internal services for faster debugging and issue resolution
previously at google (summer 2024 & 2025), where i helped modernize the play store's backend infra by modularizing services, redesigning core logic, and driving feature rollouts across high-traffic systems
in fall 2024, worked as a ta at fiu cis 3590 mentoring 45 students contributing to open-source projects and helping them polish their resumes and linkedins
i like working at the intersection of infrastructure, ai, and complex backend systems — building things that scale and solve real problems
Ian C. Borges
Education
- GPA: 3.98
- Relevant coursework: Data Structures & Algorithms, Operating Systems, Algorithms & System Design, Systems Programming in C, Introduction to Software Engineering with GenAI, Artificial Intelligence, Intro to Machine Learning
- Honors & Awards: Bright Future Florida Academic Scholar, FIU Gold and Blue, National Hispanic Merit Award
- Google Tech Exchange Scholar — completed courses taught by Google software engineers
Technical Skills
- Languages: Java, C/C++, Python, SQL, Go, TypeScript, JavaScript, HTML/CSS
- Frameworks: Spring, Flask, React, Node.js, gRPC, Protobuf, JUnit, Mockito, REST
- Technologies: Git, GCP (BigQuery, Spanner), AWS (Bedrock, Kendra), PostgreSQL
Experience
- Incoming Fall 2026 Software Engineering Intern in GCloud Apps.
- Built a full-stack platform automating hardware placement across 5+ data centers by unifying siloed capacity data.
- Engineered a Python recommendation engine validating 10K+ racks against power and space limits to prevent outages.
- Designed reusable APIs and core data infrastructure now powering 3+ downstream tools and services across teams.
- Shipped a self-serve web tool for hardware health checks, processing 1K+ link checks and replacing a CLI-only workflow.
- Built an LLM-driven diagnostic framework with AWS Bedrock to streamline internal documentation and debugging.
- Created automated jobs to index 10k+ internal code and doc files in S3 and Kendra for retrieval via a Bedrock knowledge base.
- Enabled AI agents to fetch real-time ticket and log data through an MCP layer, automating analysis for 100% of daily tickets.
- Integrated the framework into team workflows, creating a scalable base for automated diagnostics and future LLM usage.
- Designed backend infrastructure enforcing dynamic platform limits for millions of daily users with Java, SQL, and Protobuf.
- Led a staged rollout gated by experimental flags and end-to-end testing, ensuring a successful launch with 0 regressions.
- Redesigned core business logic and removed deprecated Protobuf schemas to support over 2 new requested use cases.
- Authored a technical design document outlining specific requirements, tradeoffs, and high-level feature implementation.
- Developed a new validation system for Play Store backend APIs using Java, Proto, and Guice to support 45+ use cases.
- Created a reusable and extendable utility class adopted across 15+ validation modules, reducing duplicate code by 40%.
- Implemented extensive testing with 90% unit test coverage, adding integration and end-to-end tests to ensure reliability.
- Assessed a new Protocol Buffer framework to optimize validation logic and presented key insights to the team.
Projects
- Built a full-stack web app to calculate users' carbon emissions based on daily travel routes, using a responsive frontend and Java Spring Boot backend to suggest eco-friendly alternatives via the Google Maps API with real-time route data.
- Collaborated with a team of 4 to build a full-stack Python app using Streamlit, delivering personalized fitness and nutrition recommendations via Vertex AI models based on user goals, historical usage data, and preferences.