Building scalable cloud infrastructure, CI/CD systems, and AI-ready platforms.
DevOps & Platform Engineer with experience in AWS, Azure, GPU infrastructure, observability, automation, and production systems.
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About Me
- 2.5+ years of experience in DevOps and Platform Engineering.
- Worked extensively with AWS, Azure, Docker, CI/CD, observability, and AI/LLM systems.
- Experience managing production deployments and high-availability systems.
- Strong focus on automation, reliability, scalability, and cost optimization.
- Built infrastructure for real-time AI inference workloads.
Production Experience
- Incident handling & P1/P2 support
- High availability & Auto-scaling
- Reliability engineering
- Production infrastructure ownership
Current Focus
- AI infrastructure & GPU optimization
- Platform engineering
- DevSecOps integration
- Scalable Distributed systems
Technical Arsenal
Cloud
DevOps & CI/CD
Monitoring
AI Infrastructure
Architecture
Programming
Impact & Achievements
Demonstrating value through reliability and automation.
Supported highly-available AI/LLM workloads on scalable GPU infrastructure.
Built scalable CI/CD systems with automated rollbacks and validation.
Improved monitoring and drastically reduced incident response times.
Fully automated infrastructure operations eliminating manual toil.
Featured Initiatives
BreezeStay SaaS
Problem: Needed a highly scalable, zero-downtime infrastructure for a multi-user rental platform.
Architecture: AWS EKS, RDS, CloudFront, Route53, distributed microservices.
Key Features: CI/CD automation, active monitoring, automated domain + SSL management.
Impact: Delivering robust production infrastructure for active users with 99.9% uptime.
AI Infrastructure & LLM Platform
Problem: LLM deployments were inefficient, causing high latency and inflated GPU costs.
Architecture: Dockerized AI deployments, Kubernetes orchestration, Load balanced nodes.
Key Features: GPU-based inference systems, real-time pipelines, monitoring utilization.
Impact: Massively optimized AI workloads, reducing latency and cutting cloud costs.
Observability Platform
Problem: Lack of centralized visibility into distributed cloud resources and server lifecycles.
Architecture: Prometheus scraping endpoints, Grafana dashboarding, CloudWatch integration.
Key Features: Real-time monitoring, intelligent alerting, automated lifecycle management.
Impact: Prevented outages through proactive alerts and provided single-pane-of-glass metrics.
CI/CD Automation Platform
Problem: Manual, error-prone deployment processes across multiple development teams.
Architecture: Multi-stage pipelines, automated testing gates, seamless artifact promotion.
Key Features: Jenkins / GitLab CI/CD, SonarQube integration, rollback workflows.
Impact: Achieved 100% automated deployments, entirely eliminating human error in releases.
Architecture Showcase
System designs and deployment workflows.
Professional Timeline
Associate DevOps Engineer @ Rootle Voconv Private Limited
2025 — PRESENTHardening GPU-backed AWS infrastructure for real-time Agentic AI systems.
- Reduced inference latency significantly through vLLM serving optimization.
- Built internal SRE platform for real-time GPU cost tracking and lifecycle management.
- Orchestrated 99.9% uptime SLA using multi-AZ EKS architecture.
DevOps Engineer @ Vrinsoft Technology Pvt. Ltd
2025- Scaled ECS deployments with Auto-Scaling mechanisms.
- Engineered multi-stage Jenkins rollback logic for high-traffic peak handling.
- Containerized critical application services.
DevOps Engineer @ Sigma Solve, Inc.
2023 — 2025Owned operations for 10+ production stacks spanning .NET, Node.js, and Python.
- Migrated legacy CI/CD to GitLab CI, tripling release frequency.
- Integrated SAST (SonarQube/Trivy) gates reducing critical vulns to zero.
- Reduced MTTD by 40% via custom Grafana/Prometheus observability stack.
GitHub & Contributions
Technical Writing
How I deployed LLMs on GPU infrastructure
A deep dive into optimizing vLLM for production inference and reducing overall cloud compute costs.
CI/CD Best Practices for 2026
Designing automated deployment pipelines that eliminate human error and ensure zero-downtime rollouts.
Observability with Grafana & Prometheus
Building a central SRE dashboard to monitor distributed systems and proactively manage incidents.
AWS Deployment Architecture Patterns
Standardizing scalable infrastructure using Terraform and AWS best practices.
Docker Image Optimization
Techniques for minimizing container footprint to speed up pipeline execution and enhance security.
Production Deployment Lessons
Key takeaways from handling P1 incidents and ensuring high availability during peak traffic.
Certifications
AWS Cloud Tech Essentials
edX / CourseraAWS Academy Architecting
AWS AcademyAWS SAA-C03 (In Progress)
UdemyInteractive Terminal
Type 'help' to see available commands.
Ready to scale your infrastructure?
I'm currently open for new opportunities. Let's discuss how I can bring enterprise-grade reliability to your production systems.