Ruchin Patel

As part of HubSpot's Product Rollouts team, I design and implement scalable backend systems to govern how new features are delivered to millions of customers. My work lies at the intersection of product engineering, user engagement, and platform scalability.

Feedback System – Scalable Beta Feedback Collection

• Built a high-throughput feedback platform to collect customer input on beta features before public release, ensuring product readiness, and reducing churn.
• Engineered backend using Java, Kafka, MySQL, and distributed rate-limiting, supporting feedback for hundreds of product updates at any time.
• Enabled seamless integration into the Product Updates interface, impacting thousands of customer interactions monthly.

Product Updates Recommendation

• Developed a recommendation engine using multi-threaded processing, caching, and usage analytics to suggest relevant product updates based on user behavior.
• Led cross-functional development and delivered a beta that exceeded engagement KPIs, and received strong feedback on Slack and LinkedIn.
• Formed the foundation for HubSpot’s future ML-powered feature discovery initiatives.

Weekly Product Updates Roundup – Admin Engagement at Scale

• Led the development of a scalable roundup email system, highlighting key betas and updates to HubSpot portal admins.
• Integrated asynchronously with cross-team infrastructure, proposed, and facilitated real-time enhancements.
• Developed an AI-driven summarization tool to convert unstructured HTML into concise update summaries, improving email accuracy and UI consistency.
• Achieved strong results: 57% open rate, 10.7% click-through rate — well above industry benchmarks.

Other Contributions

• Exact Release Dates: Delivered real-time rollout visibility by implementing caching and data consistency mechanisms for percentage-based ungating.
• System Modernization: Overhauled legacy beta systems for maintainability and performance; critical to company strategy.

Worked across multiple enterprise-scale projects for retail and public sector clients, delivering scalable backend services, ML solutions, and cloud-based applications.

RBS Opal – Ahold Delhaize (Retail Services)

• Built a scalable offer management system using Flask, Docker, and Kubernetes, improving operational speed by 80%.
• Designed Redis caching and MongoDB indexing, improving query speed by 40%.
• Automated data pipelines using Azure ADF improve accuracy to 99%.
• Developed an ML-based clustering system in Python to reduce data duplication by 95%.

AMS Demand and Supply

• Developed a Django-based HR allocation app that boosted project completion by 30%.
• Built a chatbot with Azure Bot SDK, reducing manual processing time by four days.
• Provided architectural and development leadership for automation initiatives.

ADA Pathways GIS

• Designed a GIS service aiding disabled transit users, reducing operational costs by 15%.
• Built a fault-tolerant backend with Java and Python, maintaining 99% uptime.
• Deployed REST APIs and web apps using Django, AWS Lambda, and Elastic Beanstalk, improving productivity and onboarding speed by over 50%.
• Delivered a computer vision POC for real-time object detection, helping to secure a major client project.