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Over 6+ years working across AWS, Azure, GCP, Kubernetes, and Python. I bridge the gap between infrastructure and AI, ensuring that production systems and machine learning models scale predictably, securely, and silently.
I'm a DevOps & MLOps Engineer who treats infrastructure as a codebase and machine learning as a first-class production citizen. I keep Terraform modules small, well-named, and commented, so on-call engineers can reason about changes under pressure.
I usually end up owning public cloud foundations, VPC layouts, EKS/GKE clusters, and CI/CD deployment paths. Recently, my focus has been on architecting MLOps pipelines—moving ML workloads to Kubernetes and building automated model deployment pipelines that data scientists actually enjoy using.
Security is folded into my daily work—wiring IAM roles and policy checks into CI so least privilege is a default, not an afterthought.
Built core AWS infrastructure using Terraform with S3 state and DynamoDB locking. Moved ML workloads from EC2 to EKS with auto-scaling to prevent job collisions. Set up GitHub Actions for model services including image builds and Helm deploys. Replaced IAM keys with IAM roles (IRSA) for least privilege.
Owned day-to-day AWS operations (EC2, RDS). Refactored legacy CloudFormation into modern, reusable Terraform modules. Enforced Git workflows for IaC. Wrote Python/boto3 tools for user automation, security audit checks, and migrated monolithic services to Serverless AWS Lambda.
Administered daily operations for GCP (Compute Engine, Cloud SQL, GKE) treating infrastructure as code via Terraform. Containerized apps via GKE, configuring Cloud Build pipelines with image signing, and managed Stackdriver alerts via SLO-style dashboards to maximize performance.
DePaul University | Chicago, IL
JNTU | India
• Intro to Generative AI Specialization
• Intro to Large Language Models
• Responsible AI Applying Principles