Headline

SRE and Cloud Engineer | ex-Oracle

About

Software Engineer focused on Site Reliability, cloud infrastructure and automation, with experience across Linux systems, networking, Kubernetes, AWS, infrastructure as code and observability. I work where infrastructure, automation and reliability meet, building systems that are easy to deploy, easy to observe and quick to recover. At Oracle, I was a Software Engineer on the Diameter Signal Routing team. I built a modular Shell and Python automation framework that cut deployment and validation time from 2–3 days to about 3 hours through pipeline-triggered execution. I provisioned bare-metal telecom servers (VLANs, network bridging, system configuration), automated large-scale validation across 100+ peer nodes and about 150K calls per run, and traced production CPU dips and I/O wait issues to their root cause. Before that, as an intern on Oracle's OCI Data Intelligence team, I built Micronaut backend provisioning workflows and helped split a monolith into modular microservices. Outside work, I build production-style platforms end to end: A Kubernetes platform that simulates football match-day traffic spikes and autoscales with HPA and KEDA. It has Prometheus and Grafana observability, k6 load testing and an AI layer that turns alerts into plain-language incident diagnoses. A live AWS platform provisioned in Terraform (VPC, multi-AZ ALB, auto scaling, RDS). It deploys through a keyless GitHub Actions pipeline using OIDC, with no long-lived credentials. Core areas I work in: -Site Reliability Engineering & Observability -Kubernetes & Autoscaling (HPA, KEDA) -Cloud Infrastructure (AWS, OCI) -Infrastructure as Code (Terraform, Ansible) -CI/CD & Deployment Safety -Linux Systems & Networking -Automation & Toil Reduction (Python, Shell) -Backend Services & APIs (FastAPI, PostgreSQL)

Location

Bengaluru

AI stack

anthropic apis

claude code

cursor

gemini

grok

githubcopilot

Work experience

Associate Software Developer at Oracle, Jul 2025 - Apr 2026 · 10 mos, Bengaluru, - Automation and; CI/CD Pipelines: Designed and deployed a modular Shell + Python automation framework that reduced release deployment and validation time from 2–3 days to ~3 hours through pipeline-triggered execution. - Bare-Metal and; Network Provisioning: Provisioned enterprise bare-metal telecom servers over SSH, configuring custom VLAN setups, network bridging, and system-level parameters for large-scale deployments. - Telecom Scenario and; Load Simulation: Automated multi-node telecom scenario testing (6 local nodes, 108 peer nodes, 432+ active connections) and high-volume traffic simulations (~150K calls/run) using Seagull and Flock - Release Analytics: Programmed end-to-end scenario orchestration, pegstat metric validation, and automated log parsing to streamline pass/fail release analysis. - Root-Cause Analysis (RCA) and; Performance Tuning: Diagnosed critical production CPU dips and high I/O wait times, tracing the root cause to qcow2 thin provisioning and synchronous disk write operations,

Project Intern at Oracle, Jan 2025 - Jul 2025 · 7 mos, On-site, -Backend Orchestration: Developed Java/Micronaut backend workflows for OCI Analytics amp; ML provisioning, implementing API-triggered, database-backed async processing. -Microservices and Refactoring: Built REST API-driven provisioning flows and refactored monolithic repos into modular microservices to improve CI/CD scalability. -Testing and Quality: Wrote unit tests using Mockito to ensure reliable microservice interactions and backend API logic

Intern at Central Tool Room & Training Centre (MSME) Government of India, Dec 2023 - Jan 2024 · 2 mos, Bhubaneswar, During my work experience, I gained knowledge in machine learning models and their practical applications. I engaged in several mini projects that provided hands-on experience and deepened my understanding of the field. Firstly, I developed a spam detection system using Naive Bayes, focusing on text preprocessing and feature extraction. For the house pricing prediction project, I implemented linear regression and gradient boosting, ensuring data cleaning, normalization, and feature engineering were meticulously performed. In another project, I created a handwritten digit recognition system using convolutional neural networks (CNNs), enhancing my skills in image processing and model tuning. Additionally, I built a newspaper genre detector with support vector machines (SVMs), emphasizing text classification techniques. Lastly, I developed a face detection system utilizing Haar cascades and deep learning models, concentrating on image preprocessing and real-time detection. Throughout these projects, I consistently applied data cleaning, normalization, and standardization techniques, ensuring robust and accurate models. This experience significantly enhanced my proficiency in machine learning and practical deployment.

Education

Bachelor of Engineering - BE, COMPUTER SCIENCE SPECIALISATION IN CYBER SECURITY, Manipal institute of technology