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Projects

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A mix of open source projects and key professional contributions across distributed systems, data infrastructure, and developer tooling.

Open Source
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Professional Highlights
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FB Feed Ranking Infrastructure
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C++
ML Infrastructure
Ranking Systems
Meta

Building the infrastructure that powers Facebook’s Feed Ranking — high-performance backend services in C++ that support ML engineers in delivering better ranking models. Working at the intersection of infrastructure and product, optimizing how billions of users see their Facebook Timeline.


AWS Billing Unbilled Usage Auditor
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Java
DynamoDB
AWS Lambda
Distributed Systems

Designed and built a system that detects unbilled usage across AWS services. Reduced false positives by 230 million monthly transactions and cut charge discrepancies by 300x — from $125,000 to $432.

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Twitter Fleet Kernel Automation
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Python
Go
Redis
Linux Kernel

Standardized hardware and software configurations for 5,000+ production hosts. Built a Python library for Canary Kernel Validation and designed automation for kernel updates at fleet scale. Resolved 140+ tickets in a single on-call week.

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Walmart Data Lake Integration
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Apache Spark
Kafka
Apache Hudi
Big Data

Led enterprise-wide Data Lake integration for recruiting data pipelines, processing data from 10+ sources using Apache Spark, Kafka, and Apache Hudi. Architected scalable solutions for real-time data processing at enterprise scale.


Technical Stack
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C++
Python
Go
Java
TypeScript
Rust
Apache Spark
Kafka
Kubernetes
Docker
AWS
Terraform
Redis
DynamoDB

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AWS Billing Unbilled Usage Auditor

·276 words·2 mins
Designed and built a distributed system that detects unbilled usage across all AWS services — reducing charge discrepancies by 300x and eliminating 230 million monthly false positives. Key Metrics # 300x Reduction in Discrepancies $125,000 → $432 230M False Positives Eliminated ~95% Alert Actionability Architecture # flowchart LR A["Usage Records\n(Billions/day)"] --> B["Smart Sampling\n& Aggregation"] B --> C["Multi-Signal\nValidation"] C --> D["Automated\nResolution"] D --> E{Real issue?} E -- Yes --> F["Alert with\nDiagnosis"] E -- No --> G["Auto-resolve\n& Log"] style B fill:#6366f1,color:#fff style C fill:#6366f1,color:#fff style D fill:#6366f1,color:#fff Technical Deep Dive # Aggregation over Brute-Force # Instead of checking every individual usage record (which generated 230M false positives), the system aggregates at the service-account-period level.

Twitter Fleet-Scale Kernel Automation

·344 words·2 mins
Built the automation and validation tooling to manage kernel updates across 5,000+ production servers at Twitter — with zero-downtime progressive rollouts and automated canary validation. Key Metrics # 5,000+ Production Hosts Weeks → Days Rollout Time 140+ Tickets in One On-Call Week Zero-Downtime Updates Architecture # flowchart LR A["New Kernel\nVersion"] --> B["Canary\nValidation"] B --> C["Wave 1\n1% Fleet"] C --> D["Wave 2\n5% Fleet"] D --> E["Wave 3\n25% Fleet"] E --> F["Full Fleet\nRollout"] C -- anomaly --> G["Pause &\nAuto-Alert"] D -- anomaly --> G E -- anomaly --> G style B fill:#6366f1,color:#fff style C fill:#6366f1,color:#fff style D fill:#6366f1,color:#fff style E fill:#6366f1,color:#fff style F fill:#6366f1,color:#fff Technical Deep Dive # Validate Before You Roll # A Python library that validates kernel safety before fleet-wide rollout: