Leading and contributing to several critical initiatives aimed at improving billing system reliability, scalability, and customer trust.

Key Contributions:

• Architected scalable side effects system managing transient database tasks with cron-based execution engine, supporting immediate/delayed actions with team-configurable monitoring; adopted by 10+ product teams with 20+ registered side effects to orchestrate feature access changes on access-based events.

• Created enterprise-scale free trial infrastructure with Django APIs serving 4 products, handling 1M+ requests/sec and 50k+ concurrent users with Redis ElastiCache and Aurora RDS, contributing to a 15% conversion rate lift and accelerating sales acquisition.

• Built scalable usage threshold and payment notification system with a config-driven approach, eliminating existing product-specific boilerplate classes and reducing code maintenance overhead while providing automated notifications for payment success/failure events and usage threshold limits reached across products.

• Designed and deployed a real-time Stripe webhook processing system with automated billing plan discrepancy detection and distributed tracing, eliminating plan mismatches and reducing incident resolution time by 50%.

• Scaled billing Memcached infrastructure and implemented Splunk cron job monitoring alerts to maintain 100% billing uptime during 5x Black Friday traffic surge.

• Delivered UK VAT compliance system within critical Black Friday/Cyber Monday deadline, enabling significant monthly revenue expansion.

Shipped customer-facing genetic report features used by 2M+ pet owners using React/TypeScript and GraphQL APIs, surfacing complex genomic insights across health, traits, and ancestry through intuitive UX.

• Identified and led a cost-optimization initiative that led to $180K+ annual cost savings (10% monthly reduction) by AWS resource rightsizing and tagging, autoscaling, and proactive Cloudwatch monitoring.

• Extended ML deployment infrastructure for Embark's dog DNA testing product by implementing Apache Airflow workflows for new ML models including the productionized Mast Cell Tumor (MCT) ML model, processing 120K+ genetic assessments/month and ~6k DNA samples/day with 99.99% uptime.

• Led productionization of Embark's Dog Age Test by researching scientific literature and recommending preprocessing optimizations to scientists, then transforming experimental Jupyter notebooks into a production Python package using NumPy vectorized operations on DNA methylation data, reducing ML feature deployment time from months to weeks following scientific advisory board review.

• Designed domain-driven PostgreSQL schemas for Illumina sequencing workflows, improving data consistency by 30% and enabling cross-functional genetic research at scale.

• Partnered with 10+ veterinarians and geneticists to validate and release health and trait algorithms, expanding available genetic insights to 250+ total while ensuring accurate genome data storage and UI presentation.

• Built and maintained production-scale C# microservices for Microsoft Intune, enabling secure mobile device management of apps on iOS and macOS across global enterprise and educational customers.

• Architected and co-owned critical microservices integrating Apple's Volume Purchase Program API, managing license counts in CosmosDB to streamline app deployment and license management worldwide.

• Led deprecation and removal of an outdated data provider, saving a DB migration and reducing codebase complexity and enhancing maintainability for the engineering team.

• Authored the Microsoft blog article, “Deploying a Batch AI Cluster for Distributed Deep Model Training”, sharing best practices with the broader engineering community.

• Architected scalable distributed ML training infrastructure on Azure Batch AI using Keras, TensorFlow, and Horovod, enabling multi-node model training for satellite image classification processing thousands of images identifying sustainable farming practices.

• Designed and implemented an advanced signal processing pipeline in R for clinical research (Project Fizzyo), developing a custom peak detection algorithm that improved pattern analysis accuracy for cystic fibrosis treatment optimization.

• Delivered React-based administrative dashboard for Fortis, a UN crisis response platform, enabling real-time sentiment analysis and media aggregation across multiple channels during humanitarian emergencies.