enériz_
Daniel Enériz

Daniel Enériz, PhD

ML Platform & Edge AI Engineer at Veridas

I work on bringing machine-learning models into production: from the compute platforms where research teams train them to the resource-constrained devices where they are deployed.

current role

Engineer at Veridas, a company specialising in digital identity verification and biometrics, since May 2024.

My main responsibility is the internal compute platform used by the research teams: Kubernetes clusters managed declaratively through GitOps with Argo CD and Helm. The aim is a reliable environment that allows researchers to focus on their experiments rather than on the underlying infrastructure.

I also continue to work on model optimization for edge deployment, the field in which I completed my doctorate.

focus areas

  • ML platform

    Design and operation of shared compute infrastructure for research teams, with reproducible environments and declarative, Git-based management.

  • Edge optimization

    Quantization, model compression and latency and memory profiling to bring models from research to embedded hardware.

  • Cloud

    Working knowledge of Google Cloud and AWS for workloads that do not run on-premises.

Platform
KubernetesHelmArgo CDGitOpsProxmox VELinux
Cloud
GCPAWS
ML
PythonPyTorchTensorFlow / TFLite MicroQuantization
Hardware
FPGA (Zynq, HLS)Arm Cortex-MFixed-point arithmetic

background

  1. 2024 → now
    Veridas
    ML platform and edge AI
  2. 2020 → 2026
    PhD in Electronic Engineering, University of Zaragoza
    Efficient neural network implementation for edge AI · GEPM group, I3A · Teaching in Digital Systems and Physical Techniques II
  3. 2020
    MSc in Physics and Physical Technologies
    University of Zaragoza
  4. 2019
    BSc in Physics
    University of Zaragoza

research

My doctoral thesis, Methodologies for Efficient Neural Network Implementation Applied to Edge AI Sensor Fusion (University of Zaragoza, 2026), addressed the deployment of neural networks on FPGAs and microcontrollers: fixed-point quantization, latency and resource trade-offs, and energy per inference. Applications included EEG-based brain–computer interfaces, heart-sound screening and food-quality monitoring.

Selected publications

All publications and theses →

beyond work

Outside work I maintain a small homelab: a Proxmox cluster with Proxmox Backup Server, video surveillance and Home Assistant. It serves as a testbed for ideas band projects. My interest in hardware goes back further, to Arduino, Raspberry Pi and 3D printing.

contact

For professional enquiries, please get in touch by email. You can also find me on:

GitHub ↗LinkedIn ↗Scholar ↗ORCID ↗