Building secure, scalable and intelligent cloud-native systems.
name: Khwaja Nawaz
location: Newcastle upon Tyne, United Kingdom 🇬🇧
current_role:
title: MSc Cloud Computing Student
university: Newcastle University
country: United Kingdom
education:
postgraduate:
degree: MSc Cloud Computing
university: Newcastle University
undergraduate:
degree: B.Tech Computer Science and Engineering
university: Dr. M.G.R. Educational and Research Institute
country: India
current_focus:
- DevOps and Platform Engineering
- Kubernetes and Cloud-Native Infrastructure
- Amazon Web Services
- Infrastructure Automation
- AI-Assisted Cloud Security
- Zero-Trust Deployment Systems
research_interests:
- Cloud Computing
- Distributed Systems
- Kubernetes Security
- AIOps
- DevSecOps
- Workflow Orchestration
- Autonomous InfrastructureI enjoy turning complex infrastructure problems into secure, automated and reliable cloud platforms.
FastAPI · Temporal · OpenAI · OPA · Docker · Kubernetes · GitHub Actions · Amazon EKS
- Kubernetes YAML structure and syntax validation
- AI-powered deployment risk analysis
- Policy-as-Code using Open Policy Agent
- Human-in-the-loop approval
- Zero-Trust deployment decisions
- Auditable Temporal workflow execution
- Automatic blocking of insecure workloads
- Temporal workflow orchestration
- FastAPI deployment gateway
- Dockerized API and worker services
- Kubernetes deployment automation
- Amazon EKS integration
- Workflow state management
- End-to-end deployment verification
The framework prevents Kubernetes workloads from being deployed directly to the cluster. Every manifest must pass YAML validation, AI-assisted security analysis, OPA policy enforcement and administrator approval before deployment to Amazon EKS.
- Implemented an end-to-end CI/CD pipeline using Jenkins and GitHub
- Automated Docker image building and publishing
- Integrated Trivy container security scanning
- Designed GitOps-based deployment using Argo CD
- Implemented Blue-Green deployment for zero-downtime releases
- Deployed containerized microservices using Kubernetes
- Built real-time monitoring using Prometheus and Grafana
- Performed load testing using Locust
- Validated Kubernetes autoscaling
- Implemented disaster recovery using Velero and Amazon S3
This project demonstrates automation, scalability, security, observability, reliability and disaster recovery in a production-style DevOps environment.
AWS · Azure · Amazon EKS · EC2 · S3 · IAM · VPC · ALB · Auto Scaling · CloudWatch
A secure Kubernetes deployment gateway combining AI analysis, OPA policy enforcement, Temporal orchestration and human approval.
| Layer | Technology |
|---|---|
| API Gateway | FastAPI |
| Workflow Orchestration | Temporal |
| Policy Enforcement | Open Policy Agent |
| AI Analysis | OpenAI API |
| Runtime | Docker and Kubernetes |
| Cloud Platform | Amazon EKS |
Zero Trust AI Risk Analysis Policy-as-Code Human Approval Amazon EKS
A production-style platform implementing CI/CD, GitOps, security scanning, observability, zero-downtime deployment and disaster recovery.
| Layer | Technology |
|---|---|
| CI/CD | Jenkins and GitHub |
| GitOps | Argo CD |
| Runtime | Docker and Kubernetes |
| Security | Trivy |
| Monitoring | Prometheus and Grafana |
| Recovery | Velero and Amazon S3 |
CI/CD GitOps Blue-Green Monitoring Disaster Recovery
A reproducible Kubernetes performance benchmarking and observability environment using MicroK8s.
| Area | Technology |
|---|---|
| Kubernetes Cluster | MicroK8s |
| Monitoring | Prometheus and Grafana |
| Node Metrics | Node Exporter |
| Kubernetes Metrics | kube-state-metrics |
| Workload | Java and Python |
| Automation | Bash and Shell Scripts |
Benchmarking Observability Load Testing Kubernetes
A real-time IoT data processing and machine-learning pipeline operating across edge and cloud environments.
| Layer | Technology |
|---|---|
| Messaging | MQTT, EMQX and RabbitMQ |
| Services | Docker Microservices |
| Processing | Python |
| Machine Learning | Prophet and TensorFlow Lite |
| Visualisation | Matplotlib |
IoT Edge Computing Machine Learning Microservices
An automated CI/CD pipeline for building, testing and deploying a Node.js application on AWS.
| Area | Technology |
|---|---|
| Source Control | GitHub |
| CI/CD | AWS CodePipeline |
| Compute | Amazon EC2 |
| Artifact Storage | Amazon S3 |
| Application | Node.js |
AWS CodePipeline Node.js Automation
☁️ Designing secure AWS and Kubernetes architectures
⚙️ Building automated CI/CD and GitOps workflows
🔐 Exploring AI-assisted cloud security and Zero-Trust systems
🐳 Improving Docker and Kubernetes production deployments
📊 Implementing observability using Prometheus and Grafana
🏗️ Advancing Terraform and Infrastructure-as-Code skills
🤖 Learning Agentic AI, MCP and LLM infrastructure automation
Newcastle University, United Kingdom
Current areas of study and research:
- Cloud-native systems
- Kubernetes and container orchestration
- Distributed systems
- DevOps and Infrastructure Automation
- Cloud Security
- AI-assisted infrastructure
- MSc dissertation research
Dr. M.G.R. Educational and Research Institute, Chennai, India
Core academic foundation:
- Software Engineering
- Programming
- Computer Networks
- Databases
- Operating Systems
- Cloud Computing
- Distributed Systems
🇬🇧 English Professional working proficiency
🇵🇰 Urdu Native / Mother tongue
🇮🇳 Hindi Fluent
🇮🇳 Tamil Fluent
🌍 Arabic Reading proficiency
📧 Email: khwajanawaz82@gmail.com
“Building secure, intelligent and scalable cloud-native systems through DevOps, Kubernetes, AI and automation.”