I am a forward-thinking Site Reliability Engineer (SRE) & DevOps Professional based in Hyderabad, Telangana, India. With 3+ years of experience bridging the gap between development and operations, I specialize in designing, automating, and securing highly available cloud infrastructures across AWS and GCP.
🚀 My Edge: I actively integrate modern AI tools (GitHub Copilot, ChatGPT, Claude, AIOps) into my daily engineering workflows to accelerate Infrastructure-as-Code (IaC) scripting, optimize cloud resource provisioning, and automate complex incident resolutions.
- 🔭 Currently working on: Advanced Kubernetes architectures, AI-augmented CI/CD pipelines, and AWS IoT ecosystems.
- 🌱 Currently learning: Predictive Observability and advanced DevOps automation technologies.
- 👯 Looking to collaborate on: Open-source DevOps tooling, scalable cloud architectures, and AI-driven workflow automations.
- 💬 Ask me about: AWS, GCP, Site Reliability Engineering, Kubernetes, and leveraging AI in DevOps.
- 📫 How to reach me: bhuvanteja24@gmail.com
Site Reliability & DevOps Engineer (3+ Years)
- AI-Augmented Automation: Leverage GitHub Copilot and LLMs to rapidly write, refactor, and document Terraform modules and Kubernetes manifests, slashing infrastructure provisioning time by 40%.
- Cloud & Containers: Architect and manage scalable microservices on AWS (EKS, EC2, Lambda) and GCP (GKE), ensuring 99.99% uptime.
- CI/CD & Deployments: Engineer robust pipelines using GitLab CI and GitHub Actions with automated testing and security scanning to maximize deployment frequency.
- Observability: Implement comprehensive monitoring stacks (Prometheus, Grafana) and utilize AI-driven anomaly detection to proactively reduce Mean Time To Resolution (MTTR).
- Tech Stack: Python, Kubernetes, Prometheus, AWS Lambda, ChatGPT API, Webhooks.
- Use Case: To reduce manual on-call fatigue by automatically resolving common infrastructure alerts (e.g., Disk Space Full, OOMKilled Pods).
- Explanation: Engineered a closed-loop remediation system. When Prometheus triggers a specific alert, it hits an AWS Lambda webhook. The Lambda function queries an LLM to validate the anomaly context, runs an automated diagnostic script, and applies the fix (e.g., clearing temporary caches or gracefully restarting the pod) without human intervention, reducing MTTR by over 60%.
- Tech Stack: AWS IoT Core, Kinesis, DynamoDB, Terraform, GitHub Actions.
- Use Case: A highly available backend designed to securely ingest and process millions of MQTT messages from industrial manufacturing sensors.
- Explanation: Designed an event-driven architecture using AWS serverless services. Utilized GitHub Copilot to rapidly prototype Terraform configurations for the entire AWS ecosystem. Set up automated CI/CD pipelines to ensure seamless updates to data processing Lambdas, guaranteeing 99.99% uptime for cross-domain data streaming.
- Tech Stack: AWS EKS, GCP GKE, Velero, Route53, Helm.
- Use Case: Ensuring zero data loss and minimal downtime for mission-critical applications during a regional cloud outage.
- Explanation: Established an active-passive cross-cloud disaster recovery architecture. Used Velero for automated, scheduled state backups from AWS EKS to an S3 bucket, configured to easily restore onto a standby GCP GKE cluster. Implemented automated DNS failover routing via Route53 to redirect traffic during a disaster event.
Cloud Providers & Services 
DevOps, SRE & CI/CD 
AI & Automation Tools 
Scripting & Programming 
