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Cloud & DevOps Engineer with hands-on experience building production-grade infrastructure and automated CI/CD pipelines. IEEE published researcher in cloud resource optimization. 3x AWS Certified. I turn manual, error-prone ops into clean, observable, GitOps-driven systems.

role:        Cloud & DevOps Engineer
focus:       Infrastructure as Code · CI/CD · Kubernetes · GitOps · Observability
certified:   AWS Cloud Practitioner · Cloud Architecting · Microservices & CI/CD
research:    IEEE Xplore — Real-Time Load-Aware Resource Allocation in Cloud Systems
status:      🟢 Open to Freelance — Cloud migrations, infra automation, DevOps consulting
response:    Within 24 hours · faizanparvez30@gmail.com

🚀 DeployWatch — GitOps Deployment Pipeline

Zero-touch Kubernetes deployments via ArgoCD + Helm on every GitHub push, with automatic rollback on failure. Full observability stack with Prometheus + Grafana monitoring CPU/memory live.

• Built GitHub Actions CI pipeline: automated tests → multi-stage Docker build → versioned push to Docker Hub
• Deployed kube-prometheus-stack via Helm; live dashboards for CPU & memory in production
• Configured HorizontalPodAutoscaler at 70% CPU threshold with liveness/readiness probes for zero-downtime deploys

Docker Kubernetes ArgoCD Helm GitHub Actions Prometheus Grafana Flask Python

🏗️ CloudForge — AWS Infrastructure Automation — Production-grade IaC

Full AWS infrastructure via Terraform across 4 custom modules — networking, compute, storage, monitoring. Built for real-world concurrent team use.

• S3 remote state backend + DynamoDB state locking for safe concurrent deployments
• Custom VPC across multiple AZs, EC2 with IAM role-based access, AES-256 encrypted EBS & S3
• CloudWatch metric alarms + SNS alerting for CPU & instance health; live monitoring dashboard

Terraform HCL AWS (VPC · EC2 · S3 · IAM · CloudWatch · SNS · DynamoDB) Docker Bash

📡 Cloud Resource Allocation Simulator — IEEE Research Tool

Python simulation modeling real-time load-aware resource allocation across cloud clusters — the tool behind my IEEE Xplore paper.

• Benchmarked 3 scheduling algorithms (Threshold-Based, Weighted Round Robin, Round Robin)
• Demonstrated 15–30% improvement in server load optimization
• Visual analytics via Tkinter for comparing algorithm performance across traffic patterns

Python Tkinter · Published: IEEE Xplore, Feb 2026

Cloud

IaC & Containers

CI/CD

Observability

Languages & Scripting

Databases

{
  "name":      "Faizan Parvez",
  "email":     "faizanparvez30@gmail.com",
  "linkedin":  "linkedin.com/in/faizan-parvez-59b8a91b9",
  "location":  "Greater Noida, India (IST · UTC+5:30)",
  "research":  "IEEE Xplore — Real-Time Load-Aware Resource Allocation in Cloud Systems",
  "status":    "open-to-freelance",
  "services":  ["cloud-migration", "infra-automation", "ci-cd-setup", "devops-consulting"],
  "response":  "within 24 hours"
}