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Devops Engineer with less than a year in Cloud & Infrastructure Automation
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Cloud & infrastructure intern with a strong background in reliability engineering, infrastructure automation, CI/CD, containerized platforms, and Linux-based systems. Experienced in designing, deploying, and operating distributed systems for AI applications, including vector databases, serverless pipelines, and microservices. Proven ability to improve system availability, reduce deployment failures, and optimize performance through automation, monitoring, and root cause analysis. Proficient in monitoring system health using Prometheus, Grafana, and CloudWatch, implementing GitOps-based continuous delivery with ArgoCD for Kubernetes, and designing systems aligned with SLA/SLO/SLI/RTO/RPO objectives.
Osmania University
Bachelor of Engineering · Computer Science
N/A – Present
Client Server Technology PVT LTD
Cloud & infrastructure intern
February 1, 2026 – Present
Hyderābād, Telangana, India
Production-Ready, 99.9%+ НА Kubernetes Platform with Canary Releases
June 29, 2026 – Present
Designed and delivered a production-grade cloud-native microservices platform structured across three independent repositories for infrastructure, applications, and GitOps-based deployments. Implemented a Terraform-based AWS infrastructure stack provisioning VPC, EKS, RDS, IAM, IRSA, and multi-environment configurations. Dev environment was deployed via the dev branch for iterative infrastructure testing before staging and production. Developed and containerized polyglot microservices (Java, Python, Node.js) with automated CI pipelines, Docker builds, vulnerability scanning, and immutable image publishing through the dev branch. Implemented Helm-based Kubernetes deployments with ArgoCD GitOps workflows for declarative environment configuration and automated state synchronization. Engineered the platform for 99.9%+ availability, enabling zero-downtime deployments and low-latency performance under peak traffic. Designed a horizontally scalable architecture capable of handling 5–10× traffic spikes without service degradation. Built a fault-tolerant, self-healing Kubernetes system eliminating single points of failure across compute and database layers. Defined recovery objectives of RTO < 17 minutes and near-zero RPO for rapid restoration and minimal data loss. Implemented OIDC federation and IAM Roles for Service Accounts (IRSA) to eliminate hardcoded secrets and enforce least-privilege AWS access. Designed Kubernetes init jobs to automate database schema ingestion and initialization during service startup. Implemented and validated canary deployment strategies for controlled production rollouts with zero user impact. Built a comprehensive observability stack enabling real-time monitoring, metrics collection, alerting, and proactive incident detection. Eliminated credential exposure risks by enforcing secure secret management and removing hardcoded secrets from services and pipelines. Introduced automated security validation gates in CI/CD pipelines to prevent vulnerable workloads from reaching production. Established a resilient and scalable platform ensuring high availability and responsiveness under real-world load. Collaborated in an open-source, multi-contributor environment, following production-grade workflows, Git branching strategies, and IaC best practices.
View ProjectAI Infrastructure Platform
June 29, 2026 – Present
Owned the reliability and deployment of a production-grade AI application handling document ingestion, vector embedding generation, and semantic retrieval. Implemented and automated AWS infrastructure using Terraform, ensuring isolation, scalability, and fault tolerance. Built serverless ingestion and processing pipelines using AWS Lambda and S3, supporting AI deployments. Implemented monitoring, alerting, and automated health checks, achieving 99.9% availability during embedding workflows with PostgreSQL (pgvector). Led troubleshooting and root cause analysis for deployment and runtime issues, introducing retry mechanisms and rollback-safe CI/CD pipelines. Optimized CI/CD pipelines by introducing caching and parallel execution, reducing build and deployment time by 30%.
View ProjectCultural Fit Analysis
The candidate's project diversity, including a general cloud-native platform and an AI infrastructure platform, shows adaptability and a broad skill set. Their experience in an open-source, multi-contributor environment and emphasis on best practices (IaC, Git branching) suggest a collaborative and quality-focused mindset. The detailed descriptions of problem-solving and optimization efforts indicate a proactive and improvement-oriented approach, which aligns well with a dynamic technical culture.
Soft Skills & Operational Fit
The candidate demonstrates strong operational fit through their detailed descriptions of incident troubleshooting, root cause analysis, and adherence to SRE principles (SLA, SLO, SLI, RTO, RPO). Their collaboration in open-source, multi-contributor environments and standardization efforts with development teams indicate good teamwork and communication skills. The focus on automation, reliability, and performance optimization aligns well with a senior DevOps role.