DevOps Engineer| Cloud | AI/ML Engineering.
I design, automate, and secure cloud infrastructure on AWS, and I'm actively building end-to-end projects that combine:

• Scalable cloud architectures (EKS, Lambda, API Gateway, RDS, DynamoDB)
• Infrastructure as Code & DevOps (Terraform, Kubernetes, GitHubActions)
• Modern data & AI/ML workflows (S3, Glue, Redshift, SageMaker, Python)

Cloud Platforms

• AWS: VPC, EC2, EKS, Lambda, RDS, DynamoDB, S3, API Gateway, IAM, CloudWatch

DevOps & Automation

• Docker, Kubernetes, Helm, Kustomize
• Terraform, GitOps (Argo CD), GitHub Actions / GitLab CI
• Monitoring & Logging: CloudWatch, Prometheus, Grafana

Security & Reliability

• IAM, least privilege, security groups, network segmentation
• DevSecOps (image scanning, policy-as-code, compliance-like setups)
• High availability, scaling, and resilience patterns

Programming & Data

• Python (automation, data processing, simple ML workflows)
• Bash scripting
• Basic SQL & data modeling concepts

• ML Pipeline on AWS (Data → Train → Deploy) – planned
  End-to-end ML pipeline using S3, Glue or Lambda, SageMaker (or self-managed), and an API endpoint to serve predictions.

• Real-time Data Processing with Stream + ML Inference – planned
  Ingest streaming data (Kinesis / Kafka), run lightweight ML inference, and push results to a dashboard or downstream system.

• Cloud Cost Optimization & Rightsizing Project – planned
  Analyze AWS usage data, identify optimization opportunities, and automate some checks/alerts.

When I build a project, I focus on:

1. Architecture first – diagrams, components, and data flow
2. Infrastructure as Code – everything reproducible (Terraform )
3. CI/CD & automation – no manual deployments
4. Security & reliability – IAM, least privilege, logging, monitoring
5. Documentation – clear README with:
   • Problem the project solves
   • Design decisions
   • Trade-offs and limitations
   • Challenges I faced & how I fixed them

• 💼 LinkedIn
• 📧 Email: [email protected]