Matej Kalc - Current Portfolio
I am Matej Kalc, a Full-Stack AI Engineer building LLM products and AI systems from prototype to production.
Full-Stack AI Engineer at NLB (opens in new tab) and founder of Timeo (opens in new tab). I balance model quality, latency, and cost under real business constraints.
- RAG and evaluation
- Agent orchestration
- LangGraph workflows
- LLMOps for GenAI
Quick facts
- Current employment
- Full-Stack AI Engineer at NLB
- Production ML experience
- 4+ years
- Personal SaaS shipped
- 1 (Timeo)
Timeline
2025 - present
Full-Stack AI Engineer - Team Lead
NLB d.d., Ljubljana (opens in new tab)
- Led an agentic document-validation system with orchestration and checkpoints, reducing manual work by 1,000+ hours per year.
- Shipped an AI report generator with web search, retrieval-augmented generation, and image generation.
- Built dynamic graph neural networks for anti-money-laundering monitoring in banking workflows.
2025 - present
Founder
Timeo (timeoschedule.com) (opens in new tab)
- Built and launched Timeo, a scheduling SaaS for small businesses.
- For pilot users, schedule preparation dropped from about 3 hours to around 20 minutes.
2024 - 2025
Data Scientist
Zurich Insurance Group, Ljubljana (opens in new tab)
- Introduced graph neural networks for automobile insurance fraud detection and improved production performance by 20% in one business unit.
- Developed Databricks workflows that reduced batch inference time by 75%.
2023 - 2024
Data Scientist (Student)
Medius.si d.o.o., Ljubljana (opens in new tab)
- Created a Python package for solar power production forecasting.
- Improved one-day-ahead forecast quality by 25% with recurrent neural networks.
Featured projects
Each project highlights context, technical approach, and measurable outcomes.
Timeo (SaaS) - Scheduling in seconds
2025Optimization-based scheduling platform that builds compliant schedules in seconds.
Outcome: Pilot users reduced schedule planning from about 3 hours to around 20 minutes.
- Optimization
- Product
- Operations AI
Automobile Insurance Fraud Detection
2024Heterogeneous graph neural network pipeline over claims, customers, and provider relationships.
Outcome: Improved production model performance by 20% in one business unit.
- Graph Neural Networks
- PyTorch Geometric
- Insurance ML
Traffic Prediction with Temporal GNNs
2023Sensor-graph forecasting model for highway traffic prediction several hours ahead.
Outcome: Delivered accurate spatiotemporal forecasts from real transportation sensor data.
- Temporal GNN
- Time Series
- Spatial Data
CTR Prediction at Scale
2022Compared neural and factorization models for click-through-rate prediction on Outbrain data.
Outcome: Built and tuned multiple model families with Bayesian optimization on HPC resources.
- CTR Modeling
- Deep Learning
- Bayesian Tuning
About
I build end-to-end AI products for production use, from orchestration and evaluation on the backend to usable frontend experiences. In regulated environments, I prioritize observability and reliability. In products, I focus on user value and iteration speed.
Core stack: TypeScript, Next.js, Python, FastAPI, PostgreSQL. Primary modeling areas: RAG, Agent Orchestration, LangGraph Workflows, LLM Evaluation, Tool-Using AI Systems.
Contact
Trieste, Italy