Co-engineered GKE Pod Snapshots to drastically reduce AI workload startup times. Presented the architecture and benchmarks at KubeCon + CloudNativeCon NA 2025.

Optimized the GKE Image Streaming feature to reduce image pull latencies by ~30%, supporting over 7.5 million nodes across 440k+ clusters.

Implemented lazy Spark initialization to optimize first-command latency in serverless mode, driving significant compute cost reductions for Databricks and its customers.

Led the architectural migration of legacy Jetty-based RPCs to GRPC-based RPCs.

Patched a critical vulnerability in Spark requests to prevent API token leakage in non-authenticated calls.

Enhanced customer experience by backporting internal context states to DBR version 13.3.

Architected a containerized API (Python/Flask, Docker, Helm) to aggregate security scan results, reducing vulnerability resolution time across the engineering organization.

Led vulnerability triage efforts by building automated tooling to convert raw scan results into actionable Jira items, deployed via AWS EKS.

Built and maintained the SecOps stack in AWS GovCloud to ensure FedRAMP compliance.

Served as the primary coordinator for external penetration testing and internal security escalations.

Enforced "Shift-Left" DevSecOps by automating Nessus, Fortify, and OWASP scans within Jenkins, integrating results directly into HDFS for SQL-based querying.

Integrated Software Bill of Materials (SBOM) generation into CI/CD, enabling precise tracking of vulnerable 3rd-party libraries.

Built a containerized benchmarking framework on GKE to measure Prometheus performance across releases, identifying critical bottlenecks before production deployment.

Results were published via Google Cloud Monitoring.

Made valuable open-source contributions throughout the duration of the internship.

Project | OSS Prombench Contribution | GMP Prometheus Contribution | Project Usage

Built a comprehensive dashboard that portrayed the security status of various products owned by different engineering teams and also helped query relevant results to reduce the turnaround time to fix the security issues. The dashboard showed the scan results grouped by Releases, Projects, Components, and CVEs, and displayed threat counts based on products' release versions and minor versions along with a time-series analysis of issues.

Developed a query builder using Python (NLP) / Django / MySQL which boosted the efficiency of the Hive query generation process (in the ETL schedule) by ~60%. Successfully tested the generated query using the Spark architecture.

Created a visualization framework for process mining and its deviation using d3 js.