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nunoferna/README.md
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Nuno Fernandes - LinkedIn Nuno Fernandes - Email

I’m an ML Platform Engineer at Devoteam and a Software Engineering Master’s student at the University of Porto. My current focus lies in architecting enterprise-grade frameworks, automating infrastructure, and building AI-driven solutions.

πŸš€ Featured Projects

Project Description Technologies
aegis-llm LLMOps API Gateway in Go optimizing GenAI workloads via Qdrant semantic caching and rate-limiting. Go, Qdrant, Redis, Prometheus
homelab GitOps-driven Kubernetes cluster utilizing ArgoCD, Terraform, and Ansible for infrastructure automation. Kubernetes, Terraform, ArgoCD, Ansible
pihole-helm Production-ready Helm chart for high-availability Pi-hole v6 deployment on Kubernetes with full observability. Helm, Kubernetes, Prometheus

πŸ‘¨β€πŸ’» Technical Arsenal

I specialize in MLOps, Cloud & DevOps, and Full-Stack Engineering, delivering robust, scalable systems.

Languages
Python Go HCL Bash TypeScript JavaScript Java SQL

Cloud & DevOps
AWS Azure GCP Kubernetes Docker Terraform Ansible ArgoCD Helm Vault GitHub Actions Prometheus Temporal

MLOps & AI
MLflow Kubeflow LangChain Qdrant

Web & Backend
Next.js FastAPI Laravel Django SpringBoot

Databases
MySQL Redis Firebase

πŸ“Š GitHub Stats

GitHub Stats

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  1. homelab homelab Public

    A Kubernetes-based homelab utilizing GitOps (ArgoCD) and Infrastructure as Code (Ansible, Terraform) to provision and manage a K3s cluster with Cilium, HashiCorp Vault, and a full observability stack.

    TypeScript

  2. aegis-llm aegis-llm Public

    LLMOps API Gateway in Go. Optimizes GenAI workloads with Qdrant semantic caching, Redis rate-limiting, and OpenTelemetry metrics.

    Go

  3. pihole-helm pihole-helm Public

    A production-ready Helm chart for deploying Pi-hole v6 on Kubernetes, featuring full FTL configuration, high availability, and built-in observability.

    Go Template 2

  4. kurz kurz Public

    Forked from ZeMendes17/kurz

    An automated reel generator that uses sentiment analysis to extract the most emotional scenes from films and TV series. πŸ† Winner: 7.1 Tech Hub Hackathon (Season 1).

    Python