CHRISTI MAHU

University of Washington – Machine Learning

Christi expanded her expertise in high-performance computing and artificial intelligence (AI) at the University of Washington. She completed a Machine Learning Certificate program, building and training deep learning models in PyTorch with GloVe embeddings for sentiment analysis, and developing a Generative AI (GenAI) Retrieval-Augmented Generation (RAG) prototype using LangChain, Chroma vector store, and Azure OpenAI. She deployed ML models on Azure infrastructure, managing GPU-enabled Azure VMs with Tesla V100, and explored Hugging Face Transformers for language modeling and fine-tuning. 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.

WANdisco – Cloud Native LiveData Migration

As Senior Director of Platform Engineering, Christi designed and implemented a distributed edge computing platform for real-time IoT data replication across millions of devices, creating the foundation for edge ML inference and real-time analytics at global scale. She architected the serverless infrastructure and Terraform IaC patterns that enabled a two-pizza team to replace a legacy platform previously maintained by 50+ engineers, reducing infrastructure costs by 50% while supporting petabyte-scale workloads. 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 integration 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

As a Principal Architect at McKinsey & Company, Christi built an Azure data and ML infrastructure accelerator using Terraform-managed resources (Data Factory, Data Lake Gen2, Databricks, Purview), Snowflake, and Python ETL frameworks, with a Backstage portal to streamline delivery. She led platform standardization initiatives consolidating fragmented data and ML engineering practices across the firm’s global delivery organization. Her team built a developer tool suite for data engineers and AI practitioners 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 transformation, validation, and filtering, plus an early-stage automated Databricks integration for scalable processing—enabling consistent client delivery across engagements. She also optimized a Kubernetes platform for a global financial client, migrating from legacy Jenkins to GitOps workflows with GitHub Actions and implementing blue/green deployments. This reversed critical delivery issues and achieved sustained platform reliability, aligning with enterprise performance expectations.

Accenture – Cloud Architecture

As a Cloud Architect in 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.

Mozilla A-Frame – glTF 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.

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 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.