About

Results-driven Site Reliability Engineer with 4+ years of expertise in designing, deploying, and operating highly available systems serving millions of transactions. Deep specialization in SRE principles including SLO/SLI definition, incident management, and automation. Expert in AWS cloud architecture, Kubernetes orchestration, CI/CD pipeline design, and observability. Proven track record reducing MTTR by 40%, decreasing deployment time by 60%, and maintaining >99.95% uptime SLAs. Strong focus on reliability, cost optimization, and mentoring teams on production excellence and disaster recovery.

Experience2

1. GS

   Technical Support Engineer (L2/L3) — SRE Focus

   Jun 2025 — Oct 2026

   Goldman Sachs

   Maintained >99.9% uptime SLA for mission-critical financial trading applications processing millions of daily transactions. Led incident response and war rooms for P1/P2 production incidents with average MTTR under 30 minutes. Conducted root cause analysis with blameless postmortems preventing recurrences. Designed intelligent alerting strategies reducing alert fatigue. Automated operational tasks using Python and shell scripting, enhancing reliability.

2. WL

   DevOps Engineer — SRE Mindset

   Nov 2021 — Apr 2025

   Wipro Limited (Citi Bank Client)

   Designed Jenkins-based CI/CD pipelines for 50+ microservices enabling zero-downtime deployments and reducing deployment time by 60%. Managed AWS services including EKS, RDS, Aurora, Lambda and implemented infrastructure as code via Terraform and CloudFormation. Architected and managed Kubernetes clusters with 100+ nodes achieving 99.95% uptime. Implemented monitoring and SLI/SLO tracking that reduced MTTD from 10 to under 2 minutes. Led incident management efforts cutting MTTR by 40% and developed automation scripts saving 500+ annual manual hours.

Projects1

• Retail Banking Microservices Platform

  Architected cloud-native DevOps solution supporting 200+ microservices with <99.95% uptime SLA. Implemented Kubernetes orchestration handling 10,000+ requests/second with intelligent auto-scaling. Reduced infrastructure costs by 35% through reserved/spot instances and resource optimization. Decreased deployment latency from 2 hours to 10 minutes through pipeline parallelization and optimization. Implemented SLO-based monitoring reducing production incidents by 50% year-over-year. Established incident management framework with <30 minute MTTR for P1 issues.

  • AWS
  • EKS
  • Kubernetes
  • Jenkins
  • Terraform

Education2

• KU

  Master of Computer Applications

  2023

  Kakatiya University

• CD

  Bachelor of Science

  2021

  Chaitanya Deemed to be University

  Statistics