I design, automate, and scale cloud infrastructure with security and reliability in mind
About
I'm a Cloud & DevOps Engineer who enjoys turning manual, fragile infrastructure into secure, automated, and observable systems.
I've worked across AWS, Azure, and GCP — building CI/CD pipelines, Terraform modules, Kubernetes deployments, and monitoring stacks used by real teams in production.
What excites me most is solving reliability problems:
- Reducing deployment risk through automated testing and progressive rollouts
- Improving developer experience with self-service infrastructure and clear documentation
- Designing infrastructure that scales without surprises — observability first, not afterthought
I hold a Master's in Management Information Systems from the University of Arizona, and I'm actively seeking Cloud, DevOps, Platform Engineering, or SRE-focused roles where I can contribute to building resilient systems at scale.
Experience
Career Progression
From data analysis to cloud infrastructure automation
Data Analyst Intern
MEDTOUREASY
Jun 2021 - Jul 2021Power BI, SQL, Data Pipelines
Cloud Engineer
LTIMindtree
2022-2024AWS, Kubernetes, Terraform
DevOps Intern
Blue Cross Blue Shield
2025CI/CD, Security, Automation
DevOps Intern
Blue Cross Blue Shield of Arizona
- Automated multi-environment AWS deployments using Terraform, standardizing infrastructure across teams
- Integrated Veracode security scans across 300+ CI/CD pipelines to proactively mitigate vulnerabilities
- Improved deployment reliability with automated smoke testing and REST API validation
Cloud Engineer
LTIMindtree
- Reduced manual deployment effort by 70% through Terraform and Ansible automation
- Optimized containerized workloads on Kubernetes (AKS, EKS) for high availability and scalability
- Built Grafana and Prometheus dashboards enabling real-time observability across cloud workloads
Data Analyst Intern
MEDTOUREASY
- Built automated reporting dashboards with Power BI and SQL Server, reducing manual reporting by 50%
- Designed data pipelines for healthcare analytics supporting 10,000+ patient records
Project Highlights
NYC Taxi Dynamic Fare Estimation Platform
Problem
Real-time fare estimation requires scalable infrastructure, low-latency APIs, and reliable deployments — none of which are trivial in a production environment. Manual deployments and lack of automated testing led to frequent downtime.
DevOps Focus
- •Containerized the ML model and API using Docker for consistent environments
- •Built GitHub Actions CI/CD pipeline with automated linting, testing, and deployment
- •Implemented Redis caching layer reducing API response time by 60%
- •Configured environment separation (dev/staging/prod) with managed PostgreSQL
YouTube Trends Analytics Pipeline
Problem
Processing large-scale YouTube data required serverless architecture that could handle variable workloads without over-provisioning. Traditional ETL pipelines were too rigid and expensive at scale.
Architecture & Automation
- •Built event-driven data pipeline using AWS Lambda triggered by S3 uploads
- •Automated data cataloging with AWS Glue crawlers for schema discovery
- •Enabled ad-hoc SQL queries on S3 data lake using Athena (no database provisioning)
- •Implemented cost optimization with S3 lifecycle policies and intelligent tiering
Bakery Demand Forecasting System
Problem
Small businesses often struggle to plan seasonal production from historical sales data. This project packages demand forecasting as a cloud service so forecasts can be generated through an API instead of manual spreadsheets.
Architecture & Deployment
- •Built a containerized forecasting service using Python and Prophet.
- •Deployed on DigitalOcean App Platform with autoscaling containers for inference.
- •Used object storage for model artifacts and scheduled batch retraining via cron. ([github.com](https://github.com/viditpawar/bakery-demand-forecasting-system))
- •Exposed forecasts through an HTTPS-secured REST API.
How I Work
Automation-First Mindset
If you're doing it manually more than twice, it should be automated. I treat infrastructure as code and deployment as a product feature, not an afterthought.
Security by Default
Security isn't a checkbox — it's integrated into the pipeline. From secrets management to RBAC, every deployment enforces least-privilege and audit trails.
Observability as a Feature
You can't improve what you can't measure. I build monitoring, logging, and alerting into every system — not as an afterthought, but as core infrastructure.
Infrastructure as Product
Platform teams exist to enable developers, not gatekeep them. I design self-service infrastructure that's documented, tested, and easy to use.
Technical Skills
Cloud Platforms
- •AWS — EC2, EKS, S3, Lambda, RDS, IAM, CloudFormation
- •Azure — AKS, App Service, Key Vault, Azure DevOps, Security Center
- •GCP — Compute Engine, BigQuery, Cloud Functions
Multi-cloud infrastructure design and migration experience
Infrastructure & Automation
- •Terraform — modules, state management, multi-environment workflows
- •CI/CD — GitHub Actions, Azure DevOps, Jenkins
- •Containers — Docker, Kubernetes (EKS/AKS), Helm, ArgoCD
- •Configuration — Ansible, Chef, Puppet
GitOps workflows and immutable infrastructure patterns
Observability & Security
- •Monitoring — Prometheus, Grafana, CloudWatch
- •Logging — CloudTrail, Azure Monitor, ELK Stack
- •Security — Azure Security Center, Vault, Veracode, SonarQube
Security-first approach with shift-left testing and compliance automation
Programming & Data
- •Languages — Python, Bash, PowerShell, SQL
- •Databases — MySQL, PostgreSQL, Oracle SQL, BigQuery
- •Analytics — Power BI, Tableau
Infrastructure automation and data pipeline development
Certifications
Get In Touch
I'm actively seeking Cloud, DevOps, Platform Engineering, or SRE-focused roles. Let's connect and discuss how I can help your team build reliable, scalable systems.