I build developer tooling at Booking.com in Amsterdam: the internal agent platform, the MCP integration platform, and the infrastructure that helps engineering ship faster. Four years at PhoenixNAP before that on backend orchestration for a bare-metal cloud, alongside an MSc at on industrial MLOps.

01Experience

  1. Apr 2025
    to Now

    Software Engineer · GenAI Engineering

    Building tools to accelerate developers across the company: the internal agent platform, MCP integration platform, and the infrastructure around them.

  2. 2023 to 2025

    M.Sc Computer Science

    Graduated with a Master's degree focusing on advanced topics in distributed systems, cloud computing, and software architecture. Dissertation: developed a real-world industrial anomaly detection pipeline for CNC machines using MLflow/Kubeflow, automating model training through deployment, with .

  3. 2021 to 2025

    Software Engineer

    Worked as a full-time Java engineer at this bare-metal cloud provider, developing automation systems and internal tools. Led projects including automated RAID configuration, custom OS image creation, and internal provisioning tools built with Spring Boot.

  4. 2018 to 2021

    Software Engineer Intern

    Developed and maintained an internal employee management tool as part of a small intern team, handling the full stack including frontend, backend, database, deployment, and support. Collaborated with Product Owners to prioritise sprint tasks and gained practical experience with Java, Spring, Maven, SQL, and JavaScript.

  5. 2018 to 2021

    B.Sc Artificial Intelligence

    Studied core AI concepts including machine learning, computer vision, and knowledge representation.

02Selected work

  1. Key finding·CAIN 2026
    No single tool covers the full MLOps lifecycle. Teams stitch several together. Interoperability becomes the central concern.
    41papers reviewed across orchestration, versioning, tracking, deployment, monitoring.
    Peer-reviewed paper

    A Systematic Review of MLOps Tools

    Micallef, Z., Rajenthiram, K., and Gerostathopoulos, I. (2026). In Proceedings of the 5th International Conference on AI Engineering (CAIN '26). ACM, Rio de Janeiro.

    Maps MLOps-native tools to lifecycle components, identifies adoption patterns, and synthesises reported limitations from real-world usage. Fed directly into the MSc thesis pipelines built on 's industrial use case.

  2. Industrial MLOps pipeline figure
    MSc Thesis

    Industrial MLOps for Anomaly Detection

    Two pipelines (one MLflow, one Kubeflow) built around an industrial anomaly-detection use case on CNC machine signals, with . Honest finding: MLflow gets you running quickly; Kubeflow needs Kubernetes fluency before it gives you anything. Both work; the choice is mostly about what the team already runs.

  3. Saliency-directed product placement
    BSc Dissertation

    Saliency-Directed Product Placement

    Computer-vision system that ranks products in a scene by predicted attention. Mask R-CNN object detection plus a custom saliency-segment ranking algorithm. 0.66 correlation with human attention on a held-out set. Python, OpenCV, Mask R-CNN, Supervisely.

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