Stuart Sul

Stanford University

M.S. in Computer Science

• Recipient of the merit-based Kwanjeong scholarship ($60,000).

Seoul National University

B.S. in Computer Science and Engineering

• GPA: 4.21/4.3 (class rank: 1st).
• Member of the College of Engineering Honor Society.

Software Engineer (DevOps & MLOps Lead)

• Engineered and maintained a resilient multi-cloud RAG infrastructure across AWS and Azure.
• Migrated all application workloads to a K8s environment and implemented IaC for existing cloud resources using Terraform.
• Enabled detailed monitoring of customers' LLM usage through the EFK stack, Prometheus, and Grafana.
• Established core DevOps practices, including GitOps, autoscaling, CI/CD, monitoring, logging, and alarming, with Argo CD, Karpenter, GitHub Actions, Elasticsearch, Fluentbit, Kibana, Prometheus, and Grafana.

Co-Founder and CTO

• Co-founded and grew Blux to serve 10M+ monthly users with $1M+ in annual revenue; Blux is an AI SaaS startup specializing in real-time recommender systems for e-commerce.
• Secured $1.7M in funding and received a $10M valuation in South Korea.
• Designed a Kubernetes-based e-commerce recommender engine that handles 1B+ requests monthly and trains/deploys 100+ ML models daily (featured in the official AWS Blog).
• Engineered a robust, scalable, multi-tenant MLOps architecture that automates data gathering, model training, deployment, and monitoring, and maintained 99.95%+ service uptime since the initial product launch.
• Achieved 90% automation of SaaS product delivery by developing microservices for customer data collection and recommender model serving using FastAPI and gRPC.
• Developed and documented the Blux SDK (Python, Java, Kotlin, JavaScript, TypeScript, PHP, and Swift).
• Built Transformer-based recommender models with TensorFlow, leading initial customers to switch from a comparable AWS product to Blux for superior click-through rates.
• Initiated and managed multiple engineering projects, including no-downtime ECS -> K8s migration, Cafe24 (Korean Shopify) product launch, and tenant onboarding automation (using Jenkins, Argo CD, and Airflow).
• Recruited, built, and led an agile team of 15+ engineers.

Research Assistant (Advisor: Prof. Jae W. Lee)

• Designed and implemented a novel embedding clustering algorithm in C++, reducing the main memory access by up to 44% in commercial deep learning recommendation models (DLRMs).
• Co-authored an academic paper accepted at ASPLOS 2021, a top-tier CS systems conference.
• Analyzed query patterns in recommendation dataset with hyperpartitioning algorithms.
• Performed support tasks including data preprocessing, analysis, and algorithmic prototyping in C++ and Python.

Research Assistant (Advisor: Prof. Kyogu Lee)

• Implemented an audio processing architecture using CNN, cGAN, super-resolution, and the Griffin-Lim algorithm, contributing to a commercial singing voice synthesis project.
• Performed data labeling on audio and MIDI data with millisecond-precision using Logic Pro and Python, preparing 30+ songs for model training.
• Led 6 lab seminars on advanced Python, teaching a group of graduate students with no prior technical background.

University Team Project

• Developed a TensorFlow-based deep learning model and React.js-based mobile application for automated gravel quality assessment in concrete manufacturing.
• The project was requested by Sampyo, a leading cement manufacturer in South Korea.

University Team Project

• Optimized LLVM compiler using C++ to enhance assembly code performance.
• Secured 2nd place among 13 teams in an LLVM optimization competition at Seoul National University.

Personal Project

• A deep learning framework written using only native Python (not even NumPy).

Personal Project

• A game of five-in-a-row, with GUI and an AI opponent.
• GUI and AI written in native Python.
• AI implemented using minimax algorithm.