MSc Computer Science graduate (TU Graz, GPA 1.5/5.0) building production-oriented AI systems that combine machine learning, computer vision, and full-stack engineering.
I focus on turning research ideas into deployable systems — including LLM pipelines, pose estimation workflows, hybrid backend architectures, and evaluation-driven ML applications.
- 📍 Austria (open to Austria / Switzerland / London / remote)
- Applied ML & Computer Vision – pose estimation, segmentation, evaluation pipelines
- LLM Systems – semantic chunking, objective generation, structured extraction, RAG-style workflows
- Full-Stack AI Applications – FastAPI backends, React frontends, database integration, deployment
LearnApp – LLM Course Generation Pipeline
Full-stack system for semantic document segmentation and learning objective induction using LLMs.
MealArchitect – LLM Recipe Generation Pipeline
Full-stack system for LLM-based recipe generation and generated visuals.
Tennis Serve Pose Analysis
Computer vision pipeline for biomechanical phase detection and automated technique feedback.
Art Gallery Web Platform
Hybrid PHP + FastAPI system with AI-assisted publishing and real-world client deployment.
Dino Demolition Physics Prototype
Custom 2D rigid body and fracture simulation engine built without external physics libraries.
Core:
Python · PyTorch · FastAPI · React · TypeScript · MongoDB · Docker
Experience With:
Computer Vision · LLM APIs · Cloud Deployment · Unity · REST APIs · Evaluation Frameworks
- LinkedIn: www.linkedin.com/in/thomas-plangger
- Email: [email protected]