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Ton Llop

InfrastructureDevOpsCybersecurityLocal AI

I build systems that keep working when things go wrong.

I’m Ton Llop, an engineering student and developer focused on infrastructure, distributed systems and backend engineering. I am currently building toward the intersection of DevOps, cybersecurity and local AI.

My projects explore how systems are provisioned, automated, secured, observed, and recovered. This includes Debian servers, containerized workers , scientific platforms, and local AI runtimes.

Explore selected work

Current direction / Infrastructure · DevSecOps · Local AI

Based in Tarragona, Spain · English / Spanish / Catalan

Focus map

  • Infrastructure & DevOpsprovision · automate · observe · recover
  • Cybersecurityharden · verify · audit
  • Local AIlocal inference · privacy · control

Foundation / Backend engineering · Distributed systems

I build and operate reliable systems, explore how to secure them, and research how AI workloads can run locally.

01Selected work

Scientific software

Lipopotamo

A modular scientific platform for processing and managing NMR data — from uploaded batches and individual sample execution to visualization, outputs and report generation.

I work on execution management across batches, samples and instances: selective runs of scientific software, asynchronous processing with specialized workers, orchestration with execution history and partial re-runs, merge strategies for scientific results, and authentication and access control across containerized services.

  • Batch, sample & execution management
  • Asynchronous workers & orchestration
  • Execution history & partial re-runs
  • Python
  • FastAPI
  • MongoDB
  • React
  • TypeScript
  • Keycloak
  • Docker Compose
Internal project

Private repository

Security tooling

Ninjadorks

A search-automation toolkit for authorized security research — building advanced queries, collecting structured results and assisting with the analysis of discovered resources.

Automates Google and DuckDuckGo searches with advanced dork construction, produces structured JSON and HTML reports, retrieves selected files and applies regular-expression and optional model-assisted analysis. Intended for authorized research, education and defensive analysis in controlled environments.

  • Advanced dork construction
  • Structured JSON & HTML output
  • Regex & model-assisted analysis
  • Python
  • Selenium
  • HTML
  • JSON
  • Regex
PublicView on GitHubCybersecurity Learning Labs
Local AI infrastructure

JaullLocal AI Checker

A local-first environment for running, inspecting and comparing AI models without depending on proprietary desktop applications or external inference services.

Currently exploring direct llama.cpp integration, model and runtime management, hardware-aware execution and local file processing — packaged as a reproducible Docker distribution with responsibilities split into specialized workers, keeping data and inference under the user’s control.

  • Direct llama.cpp integration
  • Hardware-aware execution
  • Reproducible Docker distribution
  • Python
  • TypeScript
  • React
  • Docker
  • Docker Compose
  • llama.cpp
Work in progress

Currently in development

Server administration

Debian Config

A Debian server operated as code: idempotent bootstrap and provisioning scripts, tracked configuration and systemd units, backup and restore automation with verification, and disaster-recovery runbooks.

Everything the server runs is reproducible from the repository: provisioning scripts converge to the same state on re-runs, SSH access is hardened and key-only, and scheduled backups are restore-tested so recovery is a documented, verified procedure rather than a hope.

  • systemd services & timers as code
  • SSH hardening & access control
  • Verified backups & disaster recovery
  • Debian
  • Bash
  • systemd
PublicRepository
Mobile application

Beer Fantasy

A social mobile application for tracking drinks, competing with friends and turning nights out into fantasy-style leagues and challenges.

Designed and built end to end: the Flutter app, authentication, the PostgreSQL data model on Supabase with Row Level Security and RPC functions, the social and league logic, n8n automation workflows and the full Android release on Google Play.

  • Leagues, leaderboards & weekly competition
  • Achievements & statistics
  • Moderation & responsible-use limits
  • Flutter
  • Dart
  • Supabase
  • PostgreSQL
  • Row Level Security
  • n8n
  • Android
PublishedView on Google Play

02More work

  • Scalable & Elastic Ticket Service

    Distributed systems

    Queue-based ticket processing with RabbitMQ, stateless workers and PostgreSQL on AWS ECS Fargate — idempotent, at-least-once processing with retries, dead-letter queues and backlog-based autoscaling, benchmarked for throughput and latency.

    AWS deploymentCore implementation

    RabbitMQ · PostgreSQL · AWS Fargate · Docker

  • GSX Infrastructure

    Infrastructure

    IT infrastructure deployed with Docker Compose and Kubernetes: Terraform provisioning, network segmentation with NetworkPolicies, and observability with Prometheus, Alertmanager and Grafana.

    Repository

    Kubernetes · Terraform · Prometheus · Grafana

  • Superscalar simulation & branch prediction

    Computer architecture

    Simulation of a superscalar processor and analysis of an Alloyed branch predictor, modeled in C.

    Superscalar simulationBranch predictor

    C

  • Graph centrality at scale

    Algorithms & data

    Vertex-importance and centrality measures computed over large GraphML graphs, implemented in Java.

    Repository

    Java · GraphML

All repositories on GitHub

03About

Most of my work sits between backend development and the infrastructure underneath it: designing APIs, connecting services with Docker, moving processing into asynchronous workers and measuring whether the resulting system actually scales.

That foundation is where I’m building from. I’m exploring how to secure the systems I operate — from access control and network segmentation to authorized search automation — and researching how AI workloads can run locally, close to the data they process.

I also enjoy working closer to the hardware through computer architecture and low-level C — from simulating superscalar processors to implementing a MIPS CPU in Verilog. Working across both ends of the stack helps me understand systems as a whole rather than as isolated components.

I’m based in Tarragona, Spain, studying engineering at Universitat Rovira i Virgili, and I work in English, Spanish and Catalan. Some of my professional and research work lives in private repositories.

Now

  • Engineering student at Universitat Rovira i Virgili
  • Building Jaull, a local AI runtime environment
  • Developing Lipopotamo, a scientific NMR platform

Open to

  • Internships · junior opportunities
  • Infrastructure · DevOps · backend systems
  • Cybersecurity · distributed systems
  • Local AI · scientific software

Languages

English · Spanish · Catalan

04Technical focus

  • Infrastructure & cloud

    DockerDocker ComposeKubernetesTerraformAWSPrometheusGrafanaLinux

  • Security & DevSecOps

    System HardeningBackup & RecoveryNetwork segmentationNetworkPoliciesAuthorized Security ResearchRegex analysis

  • Local AI

    llama.cppLocal model inferenceModel & runtime managementDocker-based distributionHardware-aware execution

  • Backend & data

    PythonFastAPIPostgreSQLMongoDBSupabaseRedisRabbitMQREST APIsWebSocketsAsynchronous processing

  • Frontend & mobile

    ReactNext.jsTypeScriptTailwind CSSViteFlutterDartAndroid

  • Systems

    CJavaVerilogDistributed systemsQueue-based architecturesWorker architecturesComputer architecture

05Contact

Let’s connect.

You can explore my work on GitHub, connect with me on LinkedIn or contact me directly by email.

GitHub@Ton-LlopLinkedInAntoni LlopEmailtonichi003@gmail.com