Warre Snaet
About
I’m a backend and applied AI engineer from Belgium. I graduated with distinction in AI Engineering at Howest.
My strongest work is in Python: APIs, data pipelines, computer vision, OCR, applied machine learning, Reinforcement Learning, Generative AI, and the software required to make those systems dependable. During my international internship at 2Ai IPCA, I built a medical-imaging pipeline that turns measurements embedded in echocardiograms into structured research data.
I’m open to junior cloud, backend, software, data, and applied AI engineering roles, with a particular interest in agent infrastructure and MLOps. My platform work spans AWS and Azure, Terraform, containers, Kubernetes, CI/CD, and the services around reliable AI delivery.
Experience
- AI & software engineering intern, 2Ai IPCA: International internship using real clinical imaging data. I built a Python DICOM/OCR pipeline, validation and review tools, resumable batch processing, ECG extraction, and a source-video matcher. Metadata-guided alignment reduced one representative run from about 21 hours to 23 minutes. Case study.
- Software engineer, Apolloon 24-hour run system: Paid client work. I designed and implemented a local-first race operations platform with realtime synchronisation, automatic LAN discovery, SQLite replication, and failover for unreliable event conditions. Case study.
Selected work
- AWS Fargate infrastructure with Vault workload identity: A tested Terraform architecture for private ECS Fargate workloads behind an ALB, with two-AZ networking, IAM-based Vault authentication, CloudWatch observability, autoscaling, health checks, and deployment safeguards. Architecture and infrastructure case study.
- Financial AI agent: A six-service application with FastAPI streaming, MCP tool execution, RAG, PostgreSQL, React, ChromaDB, and local Ollama inference. In this two-person academic project, I owned the backend architecture, orchestration, persistence, containers, tests, and most frontend integration. Architecture and engineering case study.
- Azure ML lifecycle: Individual MLOps coursework covering Azure ML compute, parallel data preparation, model registration, MLflow, FastAPI, Docker, Kubernetes manifests, and GitHub Actions. Case study.
- Semi-supervised learning in Rust: Bachelor research into data-efficient plant-disease classification on edge devices. The saved SSL checkpoint reached 94.90% held-out accuracy and shipped as an approximately 26 MB offline application tested on iPhone. Read the technical case study.
- Event chatbot for XPO Group: Academic team project for an external client: a full-stack RAG system using Azure OpenAI, Cosmos DB, .NET, Next.js, and Python ingestion. Case study.
- Dataset query system: A Python client-server analysis application with authentication, moderation, statistics, broadcasts, and a PySide6 interface. Case study.
Open source
- Athas code editor · Maintainer & contributor: I contribute across its React/TypeScript and Tauri/Rust codebase. Merged work includes C# language support, image diffing, Linux UI improvements, dynamic proxy ports, resource- and memory-leak fixes, and an LSP race-condition fix. Case study.