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Gabriel Fagundes
Mid-level AI/ML & Backend Engineer specializing in AI platforms and computer vision
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About
Backend engineer with hands-on experience building real-time, low-latency systems: owned the Python backend for a real-time crowd-monitoring product (top 5% at HackHarvard 2025) using OpenCV, GPU YOLO inference (PyTorch), WebRTC, and OAuth. Also has production Kubernetes/GitOps experience (Helm/Kustomize, GitHub Actions, Argo CD), Kafka-based event pipelines, and executed a minimal-downtime on-prem PostgreSQL migration to AWS EC2.
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Key Strengths
- Owned end-to-end backend architecture for real-time crowd-monitoring system (top 5% HackHarvard 2025)
- Built low-latency real-time video pipeline with OpenCV + GPU YOLO inference (PyTorch) and WebRTC streaming
- Kubernetes production deployments for microservices and AI workloads using Docker + Helm; multi-environment releases
- Implemented GitOps with Argo CD to eliminate config drift (separate app/infra repos, Helm/Kustomize overlays, automated PR-based image tag updates)
- Autoscaled spiky GPU/AI workloads using HPA with custom Prometheus metrics
- Executed low-downtime on-prem to AWS EC2 migration with parallel run/rollback strategy and controlled DB cutover (pg_dump/restore)
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