"Production-first mindset — not just building pipelines, but thinking deeply about reliability, failure modes, observability, and customer impact."

I'm a DevOps Engineer with 7+ years of IT experience, combining deep production support expertise with hands-on ownership of cloud infrastructure, automation, and CI/CD delivery. My career has been shaped by working close to customers, understanding real production pain points, and building reliable systems that scale.

Currently at IBM as part of the Global Customer Support organization, I collaborate closely with Engineering, Product Management, and SRE teams to resolve complex issues and continuously improve SLAs. I also contribute to build and release processes, DevOps methodologies, and stable, frequent releases across Linux-based distributed environments.

Additionally, I'm currently completing a DevOps Micro Internship at The CloudAdvisory Oy (Remote) — building production-grade, automated DevOps systems from scratch.

📅 November 2021 – Present | 📍 Bangalore, India

Global telecom SaaS platform supporting millions of users | 24/7 operations across APAC, EMEA & NAM

• CI/CD Pipelines: Built and maintained Jenkins pipelines with zero-downtime blue-green deployments; reduced deployment time from 45 min to 12 min with 98% success rate
• Cloud Infrastructure: Managed AWS production infrastructure (EC2, S3, RDS, CloudWatch, IAM, VPC, Auto Scaling, Lambda); implemented 25% cost reduction via rightsizing and lifecycle policies
• Automation: Developed Python (boto3) and Bash scripts for EC2 management, S3 backup, log aggregation, and operational workflows — eliminating 60% of manual operations effort
• Observability: Built CloudWatch dashboards, custom metrics, log-based anomaly detection, and Lambda automation; slashed MTTD from 15 min → under 3 min
• Incident Response: Led 50+ P1 production incidents; maintained 99.2% on-time SLA through systematic triage, cross-team coordination, and detailed RCAs
• Database Administration: Managed MongoDB replica sets with automated backup, query optimization, index tuning — achieving 99.8%+ database availability
• Security: Implemented IAM least-privilege, encryption at rest/transit, SSL/TLS certificate management, security group hardening; zero critical security findings in audits
• API Debugging: Debugged RESTful APIs — HTTP 4xx/5xx errors, JWT/OAuth token issues, TLS handshake failures, authentication flow diagnostics

📅 January 2026 – Present | 📍 Remote

Building production-grade DevOps systems from scratch in a structured micro-internship program

• Implementing end-to-end DevOps workflows: Infrastructure as Code (Terraform), containerization (Docker/Kubernetes), CI/CD automation, and cloud-native architectures
• Designing and deploying automated pipelines with monitoring, observability, and GitOps practices

📅 November 2018 – November 2021 | 📍 Bangalore, India

Distributed SaaS applications for international clients across APAC & EMEA

• Infrastructure Automation: Python & Bash scripts reduced manual deployment effort from 2 hours → 20 minutes per release
• CI/CD Management: Managed Jenkins pipelines across dev/staging/production — achieved 95%+ deployment success rate
• Database Operations: Administered MongoDB clusters with replica sets, backups, query optimization — 99.5%+ database uptime
• Monitoring: Implemented system metrics and alerting, reducing unplanned downtime by 50%
• On-Site Delivery: Delivered critical production support in Kyiv, Ukraine — managed real-time debugging during high-pressure deployments for Kyivstar inventory platform
• L2/L3 Support: Troubleshot Linux/Unix systems, application servers (WebLogic, Tomcat, Apache, JBoss), and databases via log analysis and SQL diagnostics

Jenkins Bash Python AWS CLI

Designed and implemented fully automated blue-green deployment pipeline with health checks, traffic shifting, and automated rollback.

• Deployment time: 45 min → 12 min
• Deployment success rate: 85% → 98%
• Automated validation gates eliminate manual verification

AWS CloudWatch Python Lambda Log Insights

Built comprehensive monitoring infrastructure with log-based anomaly detection, metric-driven alerting, and synthetic health checks across distributed microservices.

• MTTD: 15 min → under 3 min for critical issues
• Real-time dashboards for distributed microservices visibility
• Lambda-driven automated remediation for known failure patterns

MongoDB Linux Python Replica Sets

Architected and deployed MongoDB replica sets for mission-critical inventory management with automated backup verification and replication monitoring.

• 99.8%+ database availability with zero data loss
• Automated failover testing and alerting
• Index optimization reduced query times significantly

Python Bash Knowledge Base

Identified top 10 support issues consuming 40% of team capacity and built automated diagnostic tooling + self-service knowledge base.

• 30% ticket deflection rate
• Resolution time: 4 hours → 90 minutes for common issues
• Pre-diagnostic Python scripts that auto-classify incoming tickets

RHEL Oracle 10g MongoDB Python Node.js AWS CloudWatch

Production support for mission-critical hosted telecom platform serving millions of subscribers.

• Node.js health-check scripts for automated alert aggregation
• Reduced MTTD via CloudWatch log queries and metric-based alerts

Linux MongoDB MySQL Shell Script VMware

On-site production support in Kyiv, Ukraine — resolved critical data sync failures, batch job errors, and VM performance issues under high-pressure delivery conditions.

Cloud (AWS)           ████████████████████  Production Expert
Linux Administration  ████████████████████  7 Years Production
Python / Bash         ██████████████████░░  Advanced
CI/CD (Jenkins/Git)   ██████████████████░░  Advanced
MongoDB DBA           ████████████████░░░░  Production Expert
Docker                ██████████████░░░░░░  Proficient
Terraform / IaC       ████████████░░░░░░░░  Building Proficiency
Kubernetes            ██████████░░░░░░░░░░  Foundational + Growing
PostgreSQL            ████████░░░░░░░░░░░░  Working Knowledge

• 🏗️ Terraform — Production-level IaC modules, state management, workspaces, remote backends
• ☸️ Kubernetes — Cluster management, pod/deployment lifecycle, kubectl operations
• 🤖 AI/ML DevOps — AWS Generative AI infrastructure, MLOps pipeline patterns
• 📦 GitOps Workflows — ArgoCD, declarative infrastructure patterns

mindset:
  - production_first: "Design for reliability, failure modes, and observability from day one"
  - automation_obsessed: "If you do it twice, automate it"
  - ownership: "End-to-end responsibility — from commit to production to incident resolution"
  - customer_impact: "Every decision evaluated through the lens of user reliability and experience"
  - continuous_improvement: "Every incident is a learning opportunity"

I'm actively open to DevOps Engineer, Platform Engineer, SRE, and Cloud Infrastructure roles.