Hi, I’m Matias Duarte 👋

Solutions Architect · DevOps / Platform Architect · Cloud Engineer
Porto, Portugal 🇵🇹 · Originally from Argentina 🇦🇷

I design and operate high-scale cloud platforms: reliable, observable, secure, cost-aware, and automation-heavy.

In the last ~5 years I’ve focused on architecture and platform engineering:

• Cloud & hybrid architecture across AWS / Azure / (some GCP)
• DevOps & IaC: repeatable environments, CI/CD, infrastructure orchestration
• SRE/operations mindset: monitoring, alerting, backups/DR, incident readiness
• Data & distributed systems: large-scale pipelines, storage, and reliability patterns
• AI for Ops: internal agents, automation tools, architecture copilots

I also support a foundation with engineering/infra work (systems, automation, reliability).

I’m actively transitioning into biotech / computational biology infrastructure, learning and experimenting with:

• Protein structure & modeling: AlphaFold/OpenFold concepts and workflows
• Biological data: NCBI datasets, metadata/provenance, genomics basics
• ML infrastructure: Vertex AI patterns (and cloud ML equivalents)
• Scale + reproducibility: pipelines, provenance, and “audit-ready” execution

My angle is: build the platform + automation layer that makes biotech compute reproducible, scalable, and secure.

I’m building TaxonomyTech Solutions, a deeptech biotech initiative focused on:

• Molecular Modeling & Simulation
  Build and analyze 3D molecular models to understand structures and behavior, supporting drug/therapy discovery.

• Biological Data Analysis (Genomics / Proteomics)
  Large-scale sequence analysis, expression insights, variant workflows (client-wetlab optional).

• Process Automation
  Automated pipelines for research and biotech operations, reducing repetitive work and improving accuracy.

• Molecular Taxonomic Analysis
  Bioinformatics-based processing to understand molecular relationships and classification.

The approach blends synthetic biology + computational modeling + AI/ML and is designed to run efficiently on cloud (AWS-first).
Partnerships and collaborations are welcome — research institutes, biotech teams, and companies building in this space.

Go

Java

Python

C++

Bash

Rust (learning)

AWS

Azure

GCP

OVH

Hetzner

Docker

Kubernetes

k3s

Nomad

Terraform

Ansible

Argo CD

GitLab

GitHub Actions

Jenkins

Grafana

Prometheus

Loki

Sentry

Apache Spark

Apache Airflow

• Apache Spark pipelines, large-scale ETL, reliability hardening
• KV / storage ecosystems (incl. internal stacks like RocksStore / RocksStoreWideColumn)
• Service and metrics platforms (incl. internal systems like Aperture / Counter)
• GitOps patterns with Argo CD, multi-cloud orchestration, cost controls

• Design for failure: redundancy, safe deploys, graceful degradation
• Everything is observable: metrics + logs + traces + meaningful SLOs
• Automation by default: if it’s repeated, it’s codified
• Cost-aware engineering: scale and keep cloud spend predictable
• Documentation matters: runbooks, diagrams, “why” captured next to “how”

• Cloud architecture for production platforms (AWS/Azure/hybrid)
• Containerized deployments (Docker/Kubernetes), plus Nomad/k3s where it fits
• CI/CD at scale (GitHub Actions, GitLab, Jenkins, TeamCity)
• DR + backups across regions, plus incident readiness & monitoring stacks
• Data pipelines and infra automation for high-throughput systems

• 🇦🇷 Spanish (native)
• 🇬🇧 English (advanced)
• 🇵🇹🇧🇷 Portuguese (native)
• 🇮🇹 Italian (intermediate)
• 🇫🇷 French (intermediate)