Cloud & DevOps Engineer focused on resilient, reproducible infrastructure and MLOps automation. I build CI/CD pipelines, Kubernetes/Helm delivery, and observability stacks that run locally or in the cloud-with zero vendor lock-in for demos and testing.

• Kubernetes (EKS/OpenShift) · Terraform/IaC · CI/CD (GitHub Actions/Jenkins)
• Docker/FastAPI · Observability (Prometheus, Grafana) · MLOps (MLflow)

• Reproducible IaC and delivery workflows with automated validation, linting, and releases.
• Serverless patterns on AWS emulated locally (LocalStack) for fast, cost-safe integration tests.
• Hands-on observability for apps and models: metrics, dashboards, basic drift checks.
• Experiment tracking and artifact versioning with MLflow for traceable ML lifecycles.

• Terraform Local Demo - Reproducible infra without cloud providers (local + random). CI with fmt and validate.
  

• AWS Serverless Local Demo - Serverless “without the cloud”: S3 + SQS + DynamoDB on LocalStack, with integration tests and CI in GitHub Actions.
  

• Helm Chart Skeleton - Chart skeleton for K8s deployments with CI (lint + template) and automated releases.
  

• Kubernetes Admin Ops Kit - Runbooks-as-code for K8s admins: Ansible playbooks (cordon/drain, ordered restarts, rollback), Helm chart (api/worker/nlp with probes, PDB, NetworkPolicy), and CI with KinD.
  

• FastAPI Observability Demo - FastAPI service with /metrics, tests, and CI (pytest + Docker build).
  

• GitHub Observability Demo - Prometheus + Grafana stack + exporter for GitHub metrics.
  

• ML Mini Pipeline - Mini ML pipeline (scikit-learn) that generates synthetic data, trains, and publishes artifacts (model.pkl, metrics.json) with CI.
  

• MLflow Mini Experiments - Experiment tracking with MLflow: parameters, metrics, and models; reproducible CI; optional local UI with Docker Compose.
  

All repos are free of corporate/cloud dependencies; everything runs locally or fully online via GitHub Actions.

• IaC: Terraform (modules, workspaces), policy-as-code basics, validations in CI
• Kubernetes: Helm packaging, rollout strategies, PDB/NetworkPolicy, KinD for CI
• CI/CD: GitHub Actions, Jenkins, semantic versioning and automated releases
• Observability: Prometheus exporters, Grafana dashboards, health checks
• Python & APIs: FastAPI services, pytest, Dockerized builds
• MLOps: MLflow tracking, artifact/version management, basic drift monitoring

• Google Scholar
• GitHub