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Devops Engineer with less than a year in AWS, Kubernetes, and CI/CD for cloud infrastructure automat
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Highly technical DevOps & Cloud Engineer with solid expertise in automating software delivery pipelines, architecting resilient cloud infrastructure, and orchestrating containerized workloads. Advanced proficiency in AWS, Docker, Kubernetes, Terraform, and Jenkins. Specialized in implementing GitOps workflows, writing declarative Infrastructure as Code (IaC), securing pipelines with automated vulnerability scanners (Trivy, SonarQube), and setting up comprehensive observability platforms using Prometheus and Grafana.
Nehru Institute of Technology
BE · Computer Science Engineering
August 1, 2021 – June 30, 2025
High-Availability AWS Cloud Infrastructure & CI/CD
June 1, 2026 – Present
Provisioned a multi-tier, secure AWS cloud environment using Terraform, featuring public/private subnets, custom Route Tables, NAT Gateways, and an Application Load Balancer. Designed a declarative Jenkins CI/CD pipeline that automatically triggers on git commits, executing static code analysis (SonarQube) and building production-ready Docker containers. Optimized Terraform state management by utilizing Amazon S3 for state storage and DynamoDB for distributed state locking, enabling secure, collaborative team execution. Secured public-facing web applications by deploying an Nginx Reverse Proxy with custom SSL termination, rate-limiting, and hardened security group headers.
View ProjectSecure Multi-Tier CI/CD Containerization
June 1, 2026 – Present
Containerized a multi-tier web application (NodeJS, Express, MongoDB) using multi-stage Docker builds, shrinking the base application image size by 62%. Integrated Trivy container image scanning into the automation pipeline, preventing the deployment of high and critical vulnerabilities to production registries. Configured local development and staging orchestration scripts using Docker Compose, establishing bridge network isolation to secure backend database ports.
View ProjectEnterprise Kubernetes Observability & GitOps Delivery
June 1, 2026 – Present
Orchestrated a highly available Python/Django web application on a Kubernetes cluster, utilizing Helm Charts to package and version-control microservice configurations. Established comprehensive cluster observability by deploying Prometheus and Grafana, constructing custom dashboards to monitor pod CPU, memory consumption, and network I/O. Configured a self-healing deployment strategy incorporating Kubernetes Liveness and Readiness probes, reducing system downtime during application updates to absolute 0%. Implemented secure configuration management using Kubernetes ConfigMaps and base-64 encoded Secrets, mounting them as environment variables inside pod definitions. Designed and mapped persistent volume storage classes to ensure PostgreSQL database state preservation across pod scheduling lifecycles.
View ProjectDocker Essentials & Kubernetes Essentials
IBM
June 1, 2026 – Present
IBM Cloud Essentials
IBM
June 1, 2026 – Present
Python Essentials
Cisco
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a strong alignment with modern DevOps practices, including automation, containerization, cloud infrastructure, and observability. The breadth of technologies covered (AWS, Docker, Kubernetes, Jenkins, Terraform, Prometheus, Grafana, GitOps) indicates a willingness to learn and adapt to various tools, which is a positive cultural fit for dynamic engineering teams. The academic background and certifications further support a continuous learning mindset. However, the lack of professional experience means team collaboration and real-world problem-solving under pressure are unproven.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive approach to problem-solving and a focus on best practices (e.g., optimizing Terraform state, implementing self-healing deployments, securing applications). The academic nature of projects suggests a strong learning aptitude and ability to apply theoretical knowledge to practical scenarios. However, without professional experience, the operational fit in a real-world, fast-paced environment is yet to be validated.