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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. It handles everything from uploaded batches and single-sample runs to plots, outputs and reports.

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. It builds advanced queries, saves the results in a structured format and helps you analyse whatever turns up.

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 LLM qualification

Checks whether a local LLM will really work on a given machine, starting from the hardware it actually has and ending with benchmarks measured on it.

Jaull analyses local hardware, selects candidate model configurations, resolves and verifies the exact artifact that would run, validates runtime and backend readiness, executes the model locally and compares its predicted requirements against real benchmark measurements. Workload-aware qualification and reproducible deployment are the direction it is heading, not what it does today.

  • Hardware-aware model qualification
  • Artifact verification & provenance
  • Real execution & benchmark evidence
  • Prediction vs observed performance
  • Python
  • Typer
  • Textual
  • llama.cpp
  • GGUF
  • Hugging Face
Work in progressView on GitHub
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. Processing is idempotent and at-least-once, with retries, dead-letter queues and autoscaling based on the queue backlog. I benchmarked it 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’s the base I’m building on. Lately I’ve been learning how to secure the systems I run: access control, network segmentation, authorized search automation. I’m also digging into how AI workloads can run locally, right next to the data they use.

I like getting close to the hardware too. I’ve written low-level C, simulated superscalar processors and built a MIPS CPU in Verilog. Spending time at both ends of the stack helps me see a system as one thing instead of a pile of separate parts.

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, an evidence-driven local LLM qualification toolkit
  • 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 managementArtifact verification & benchmarkingHardware-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