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Matej Kalc - Current Portfolio

I am Matej Kalc, an AI Engineer building LLM products and ML systems from prototype to production.

ML/AI Engineer at NLB and founder of Timeo. I balance model quality, latency, and cost under real business constraints.

  • RAG and evaluation
  • Graph neural networks
  • Production ML operations
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Current employment

ML/AI Engineer at NLB

Production ML experience

4+ years

Personal SaaS shipped

1 (Timeo)

Timeline

  1. 2025 - present

    ML/AI Engineer - Team Lead

    NLB d.d., Ljubljana

    • 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.

  2. 2025 - present

    Founder

    Timeo (timeoschedule.com)

    • Built and launched Timeo, a scheduling SaaS for small businesses.
    • For pilot users, schedule preparation dropped from about 3 hours to around 20 minutes.

  3. 2024 - 2025

    Data Scientist

    Zurich Insurance Group, Ljubljana

    • 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%.

  4. 2023 - 2024

    Data Scientist (Student)

    Medius.si d.o.o., Ljubljana

    • 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

2025

Optimization-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
Open proof

Automobile Insurance Fraud Detection

2024

Heterogeneous 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
Open proof

Traffic Prediction with Temporal GNNs

2023

Sensor-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
Open proof

CTR Prediction at Scale

2022

Compared 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
Open proof

About

I build applied AI systems for production use, with emphasis on robust evaluation and practical deployment. In regulated environments, I prioritize observability and reliability. In products, I focus on user value and iteration speed.

Core stack: Python, PyTorch, PyTorch Geometric, Databricks, SQL. Primary modeling areas: Graph Neural Networks, Time Series, NLP, RAG, Forecasting.

Contact

matejkalc99@protonmail.com

Trieste, Italy

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