available for 2027 new-grad positions

Gaurvi Arora

Solutions Architect

Seattle, WA/ AWS Certified
view projects→resume
— gaurvi — -zsh — 80×24
gaurvi@space:~$
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01 // cat about.md

About

I'm a Solutions Architect and cloud engineer focused on the unglamorous work that keeps systems fast, cheap, and reliable — multi-region AWS design, infrastructure automation, and observability that turns noise into a single signal.

Currently pursuing my M.S. in Computer Science at Northeastern University (2027) after a B.Tech in CS. Recently I re-architected a multi-region AWS platform, drove a 20% cut in monthly cloud spend, and shipped an LLM-powered AIOps tool that reduced mean time to resolution by 40%.

I like problems where infrastructure, cost, and developer velocity intersect — and I measure my work in latency, dollars, and MTTR.

AWSKubernetesTerraformCI/CDAIOps
0%
provisioning time cut
5 hrs → <45 min
0%
monthly cost reduction
idle EC2/RDS bloat
0%
release cycle faster
parallel CI gates
0%
MTTR reduction
LLM AIOps tool
02 // tail -f deploy.log

Experience

Software & Cloud Engineering Intern@ Cloud Analytics
3-month internship · Gurugram, India
AWSEC2IAMVPCRDSCI/CDLLM / AIOps
  • ✓[01]Diagnosed recurring infrastructure provisioning failures via stakeholder interviews and usage-data analysis; re-architected a multi-region AWS setup (EC2, IAM, VPC, RDS) that cut provisioning time from 5 hrs to under 45 minutes.
  • ✓[02]Identified idle EC2/RDS resource bloat as the root cause of cost overruns through end-to-end pipeline analysis; delivered a prioritized remediation brief adopted by engineering and finance, driving a 20% monthly cost reduction.
  • ✓[03]Defined event-tracking schemas and CI/CD health KPIs; redesigned release workflows with parallel jobs and automated test gates, cutting release cycle time by 67% and improving deployment reliability.
  • ✓[04]Scoped, built, and shipped an LLM-powered AIOps diagnostic tool for IAM/VPC root-cause detection; aligned cross-functional stakeholders on requirements and tracked launch outcomes against success metrics, reducing MTTR by 40%.
  • ✓[05]Replaced five disconnected reporting tools with a unified operational dashboard consolidating cost, latency, and usage KPIs, accelerating engineering decision cycles to a single performance signal.
exit 0 — process completed successfully
03 // kubectl get deployments

Projects

PeakPilot-AI

Predictive autoscaling platform for Kubernetes

✓COMPLETED
-80%
scaling lag
10 min
forecast lead
500 VUs
load test
  • ▸Designed a predictive autoscaling system using Prophet time-series forecasting integrated with KEDA custom metrics to generate pod-count recommendations 10 minutes ahead of demand spikes — reducing scaling lag by 80% versus reactive HPA baselines.
  • ▸Engineered a full CI/CD pipeline (GitHub Actions → Docker → Minikube) and resolved HPA/KEDA conflicts, Docker memory-allocation limits, and port collisions to achieve a stable multi-component production build.
  • ▸Instrumented a four-panel Grafana observability dashboard tracking pod count, CPU, memory, and readiness; executed A/B-equivalent load tests at 500 concurrent VUs with k6, translating results into performance SLA recommendations validated against Prometheus metrics.
KubernetesKEDAProphetDockerGitHub ActionsGrafanaPrometheusk6

MiraWear

Cross-platform wearable notification bridge

✓COMPLETED
<2 s
e2e latency
6 phases
roadmap
Android ↔ watchOS
platforms
  • ▸Architected and shipped a distributed notification relay bridging Android and Apple Watch across OS boundaries — Android NotificationListenerService → Firebase REST → headless iPhone relay → WCSession/WatchConnectivity — achieving sub-2-second end-to-end latency in Phase 1.
  • ▸Designed a six-phase product roadmap (REST polling → Firebase SDK/WebSocket → QR pairing → E2E encryption → HealthKit → background persistence) with measurable success metrics per phase including delivery reliability, latency SLAs, and pairing success rate.
  • ▸Conducted competitive analysis across Bridge (Orienlabs), AirBuddy, and cross-platform wearable tools; documented architecture failure modes and non-goals to de-risk design decisions before the Phase 2 build.
AndroidKotlinFirebaseSwiftWatchConnectivityRESTWebSocket
more on GitHub
04 // terraform graph

Tech Stack

root_module// 4 provider groups

Cloud & Infrastructure

module.cloud
AWS (EC2, IAM, VPC, RDS, EKS)KubernetesDockerTerraformAnsibleOpenStackLinux / Unix

Observability & SRE

module.sre
PrometheusGrafanaKEDACI/CDGitHub ActionsLog IntelligenceAIOps PipelinesMTTR Reduction

Languages & Tools

module.lang
PythonSQLREST APIsFirebaseKotlin (Android)SwiftGitJira

AI / ML Integration

module.ai
LLM IntegrationAgentic AIPrompt EngineeringEvent TrackingInstrumentation Schema Design
05 // aws iam list-credentials

Certifications & Education

verified_credentials

AWS
AWS Cloud Architect
Amazon Web Services
✓ valid
AWS
AWS Cloud Foundations
Amazon Web Services
✓ valid
AWS
AWS Technology Architect
Amazon Web Services
✓ valid

education.timeline

2025 — 2027
M.S. in Computer Science
Northeastern University
Seattle, WA
2021 — 2025
B.Tech in Computer Science
Rajiv Gandhi Technical University
India
06 // ssh gaurvi@space

Get in touch

I'm actively looking for cloud / DevOps / solutions-architecture internships and new-grad roles. Whether it's infrastructure, reliability, or building something ambitious — my inbox is open.

say hellodownload resume
contact.sh
--emailgaurvia359@gmail.com↗--githubgithub.com/Gaurvi11↗--linkedinin/gaurvi-arora↗
gaurvi@space:~$ _