Blahaj PI – Open-Source Content Analysis for Safer Online Spaces
Christi developed Blahaj PI, a C++23 machine learning command line interface (CLI) to identify and filter harmful content targeting transgender and non-binary individuals. The project leverages Neural Networks for Natural Language Processing (NLP) sentiment analysis to enable content classification and visualization tools, including a dynamic word cloud, for deeper insights into online discourse.
With a focus on performance, accuracy, expandability, and collaborative development, Blahaj PI features configurable models, batch processing capabilities, and detailed confidence scoring for content evaluations. The project is actively evolving, with plans to expand real-time social media scanning and improve accessibility for broader community use.
GAI Keep – LLM Content Archiver with AI-Powered Discovery
GAI Keep (Generated Artificial Intelligence Keeper) is a ground-up rewrite for capturing and organizing responses from Large Language Models (LLMs). It allows users to preserve specific AI-generated content by saving entries as timestamped JSON files, each optionally tagged with user-defined labels.
Additional functionality in prototype: behind the scenes, GAI Keep continuously scans the local entry store and applies unsupervised machine learning using neural networks and NLP. These techniques generate new, AI-inferred tags by clustering semantically similar content and surfacing hidden themes or relationships across entries. The system then adds these tags alongside the user’s, helping users reflect on and explore their stored LLM knowledge in novel ways.
Though currently in its early scaffolding phase, GAI Keep lays the foundation for a full-featured system that merges AI with personal memory.
University of Washington – Advanced Software & Machine Learning
Christi is currently expanding her expertise in high-performance computing and artificial intelligence (AI) at the University of Washington. She is enrolled in a Machine Learning Certificate program, focusing on supervised and unsupervised learning, neural networks, optimization techniques, and AI model deployment. The program covers data preprocessing, feature engineering, hyperparameter tuning, Natural Language Processing (NLP), Deep Learning (Neural Networks), and other real-world AI applications.
To complement her AI studies, she is also pursuing a C++ Programming Certificate at UW, advancing her knowledge of memory management, concurrency, template metaprogramming, and performance optimization for modern C++ (C++20 and C++23).
Previously, Christi completed the UW Embedded & Real-Time Systems Programming Certificate, where she gained expertise in low-level systems programming in C, real-time constraints, embedded architecture, and hardware-software integration.
Cirata (formerly WANdisco) – Cloud Native LiveData Migration
As Senior Director of Engineering, Christi led development of the next-generation of WANdisco’s LiveData platform—a Cloud Native system expanding replication capabilities from data center-to-cloud to cloud-to-cloud and IoT-Edge-driven migration. She managed teams of engineers, cloud architects, and designers across infrastructure, UX/UI, and automation.
A key component of this platform was a novel Linux agent deployed on IoT and Edge devices. Christi took technical ownership and refactored it from Go to Rust with full test coverage, leveraging Rust’s built-in testing framework to ensure reliability. This transition improved performance, memory safety, and concurrency while making the agent production-ready through test-driven development (TDD).
The agent supported over-the-air embedded system upgrades and real-time data transfers, enabling fault-tolerant synchronization between distributed devices and serverless cloud functions. This Rust implementation utilized Tokio for async networking, Serde for serialization, and was optimized for cross-compilation to embedded environments.
McKinsey & Company – AI & Data Engineering Platforms
As a Principal Architect at McKinsey & Company, Christi led enterprise data engineering initiatives focused on scalable data pipelines, AI tooling, and automation frameworks. She developed internal platforms and accelerators that streamlined ETL processes, machine learning workflows, and cloud-based analytics for Fortune 500 clients.
Her team built a developer tool suite for data engineers and AI practitioners, using Python and Terraform for data infrastructure automation on Microsoft Azure. The accelerator provided Azure-native solutions, integrating Purview for metadata governance, Azure Data Factory for pipeline orchestration, and Snowflake scaffolding scripts. It included sample ETL pipelines with Python-based data transformation, validation, and filtering, plus an early-stage automated Databricks integration for scalable processing.
Christi was promoted to Global Product Owner after her accelerator program gained widespread adoption across McKinsey’s US, European, and Australian data engineering teams. At a firm where leadership roles were primarily client-facing, leading an internal engineering initiative was a rare opportunity. Her program reshaped data platform strategy, proving the value of engineering enablement in a top consulting firm.
Accenture – Cloud Engineering & Automation
As a Cloud Architect and Go SME at Accenture’s Google Cloud Business Group, Christi led globally distributed teams of SDETs and SREs, designing and implementing testing frameworks in Go and Python to enhance resilience, redundancy, and reliability across cloud-native environments. She drove Kubernetes, Docker, and Terraform initiatives for Fortune 500 clients.
She leveraged Go Modules for dependency management, Cobra and Viper for CLI tooling, Gin for API development, and Testify to promote TDD and robust unit testing. She also architected gRPC-based microservices with Protobuf and implemented Kube Monkey for chaos engineering, automating pod termination to strengthen system reliability.
As part of a multi-team initiative, Christi’s team was responsible for building the Kubernetes foundation for an AI startup in Life Sciences (acquired by Accenture), a collaboration with Google Cloud. Her team focused on Helm for deployment automation, Istio with Google Cloud Anthos for service mesh networking, BigQuery for cloud-scale data analytics, and GitLab for CI/CD pipelines, optimizing cloud-native operations.
[Gopher avatar created with gopherize.me]
Mozilla A-Frame – glTF Component Development
As a contributor to Mozilla’s A-Frame, Christi collaborated with the Mozilla XR team to develop the glTF component, a streamlined alternative to the previously mainstream Collada format. Optimized for WebGL and real-time rendering, glTF significantly reduced file sizes, load times, and rendering overhead, making it the preferred format for 3D asset delivery in A-Frame.
Working closely with the original creator of glTF, Christi implemented native support for the format, ensuring seamless integration with A-Frame’s entity-component system. Her work improved performance, compatibility, and accessibility for developers creating web-based VR and AR experiences.
Mozilla Unity WebXR Exporter
Before the official Unity WebXR Exporter was developed as an in-house project by Mozilla’s XR team, Christi collaborated with developers in an experimental environment, contributing to an early open-source prototype for bringing Unity projects into WebXR. This project aimed to overcome Unity’s WebGL performance limitations, including long load times, high memory overhead, and inefficient rendering pipelines.
Her prototype explored direct WebVR API integrations, streamlined asset handling, and memory optimizations to improve real-time VR performance in the browser. Mozilla developers recognized the value of these contributions, and as the project progressed, Christi transitioned her repository to align with the official Mozilla implementation, helping shape the final direction of the Unity WebXR Exporter.
We Are Royale – Pokémon AR/VR
As the sole 3D developer at We Are Royale, Christi built the entire Unity-based codebase for the Pokémon AR/VR project, developing a modular component system in C#. Using Unity’s prefab system, she created reusable MonoBehaviour scripts, custom ScriptableObjects, and editor extensions that allowed artists to integrate assets seamlessly.
This project was a pitch and discovery-phase prototype, designed to showcase the studio’s capabilities in AR/VR development. Christi’s work included custom physics interactions, procedural animation controllers, and shader-driven visual effects to enhance real-time immersion. She also optimized scene management, object pooling, and asynchronous loading to ensure smooth performance across various hardware constraints.
Early Coding — From BASIC Chatbots to C++ Exploits
Christi’s journey in software began around 11 years old with a TRS-80 Pocket Computer handed down by her mother, an Electrical Engineer. Christi wrote her first programs in BASIC—starting with a chatbot as a virtual girlfriend, then gradually teaching it to become a study companion. By 13, she wrote a turn-based ASCII Space Invaders clone in Borland C++.
Her curiosity about networking led her to explore system administration at her school, where she wrote a C++ program that quietly traveled across the network, collecting credentials along the way. Once every student’s login was gathered, it triggered a colorful ASCII message to a particular classmate: “Will you go out with me?” The message got her attention—but ironically, Christi’s social programming hadn’t advanced as far as her technical skills, and she was too frozen to respond.
A year later, Christi and a friend discovered Linux and uncovered an unprotected password file at a local ISP. Using a specialized security tool, they cycled through various accounts, bypassing restrictions to obtain unlimited internet.
These early experiments in automation, security, and system programming laid the foundation for a career spanning game development, cloud architecture, and AI-driven content analysis—bringing her adolescent dream of chatbots to reality.
