Ramanareddy Vudumula

I'm a creative and energetic Senior Full Stack Engineer who manages all aspects of the software development lifecycle - from architecture and API design to cloud deployment and DevOps automation - for enterprise and mid-sized organizations. Self-motivated and hands-on, I thrive in agile team environments where I can own complex problems end to end. With 11+ years of experience across telecom, healthcare, and fintech, I bring deep technical range and a consistent track record of delivering measurable results. What I do best: 🔷 Full Stack Engineering - C#, .NET 5/6/8/10, Angular, React.js, TypeScript, Python, Groovy. Comfortable across every layer of the stack. ☁️ Dual-Cloud Expertise (Azure + AWS) - At AT&T, I work across both Azure and AWS daily. On Azure: AKS, App Service, Azure Functions, Key Vault, Entra ID (AAD), Service Bus, NServiceBus, DevOps Pipelines, VMSS, Blob Storage, Application Insights, Azure Monitor. On AWS: EC2, S3, Lambda, ECS, Step Functions, Amazon MQ, RDS, CodePipeline/CodeBuild/CodeDeploy, CloudFormation, SNS, Elastic Beanstalk. ⚙️ Microservices & Integration - gRPC, WCF, MuleSoft APIs, RabbitMQ, Kafka, Azure Service Bus, NServiceBus - event-driven and message-driven architectures at scale. 🔐 Security & Identity - OAuth2, OIDC, SAML 2.0, SSO, JWT, RBAC, Azure Key Vault, Active Directory, LDAP. 🤖 AI-Augmented Development - Daily use of GitHub Copilot, Azure OpenAI (GPT-4), and Cursor AI. I integrate AI into dev workflows to accelerate delivery and reduce operational work. 📊 Observability - ELK Stack (Elasticsearch, Logstash, Kibana), Zipkin, Datadog, Splunk, CloudWatch, Application Insights. Career Highlights: ⚡ Reduced API response time by 35% at AT&T via Redis caching & query optimization 🏥 Automated deduplication of 500K+ medical records via serverless AWS Lambda at H1 Insights 🔄 Cut new team onboarding from 3 days → under 2 hours using Azure Functions automation 🔁 Migrated 100+ ADO repositories to GitHub Enterprise, converting all pipelines to GitHub Actions 📉 Reduced data pipeline runtime by 40% with Python ETL automation on AWS Medium: https://ramanareddy-v.medium.com YouTube: https://www.youtube.com/@ramanareddyv6042/videos