Rajvardhann Singh

Intern

Bengaluru, Karnataka, India1 yr 4 mos experience

Most Likely To Switch

Key Highlights

Modernized billing platform handling $1B+ revenue.
Developed productivity tracking tool for cross-team efficiency.
Automated floor plan drawing using advanced neural networks.

Stackforce AI infers this person is a SaaS and Fintech backend developer with strong machine learning capabilities.

Contact

rsingh@surveymonkey.com LinkedIn

Skills

Core Skills

MicroservicesBackend DevelopmentUser ExperienceFull Stack DevelopmentMachine Learning

Other Skills

AJAXAPI DevelopmentAWSAlgorithmsAmazon SQSAmazon Simple Notification Service (SNS)Amazon Web Services (AWS)Apache KafkaBack-End Web DevelopmentBootstrapBusiness Case PreparationC++Cascading Style Sheets (CSS)Code RefactoringConfluence

About

Currently helping SurveyMonkey’s billing & payments platform handle $1B+ revenue across 205+ countries (yes, if your invoice doesn’t go through, I may have had something to do with fixing it 👀). Before that, I was building trading pipelines at Syfe, productivity dashboards at Grip Invest, and even 3D models of rooms at Homelane by teaching neural nets to read floorplans (basically convincing AI to become an architect). My toolkit includes: ⚡ Backend stacks → Python, Kotlin, Node.js, TypeScript, Spring, NestJS, Pyramid, Django ⚡ Databases I know how to use → MySQL, MongoDB, PostgreSQL ⚡ Fancy ML/AI buzzwords I learn → PyTorch, OpenCV, Scikit-Learn ⚡ DevOps-ish survival skills → AWS, Docker, Kafka, GitHub Actions I’ve also had my fair share of hackathons, late-night debugging sessions. Always looking to work on products that mix scale, impact, and a dash of chaos. 📬 Reach me @singhrajvardhann@gmail.com

Experience

1 yr 4 mos

Total Experience

8 mos

Average Tenure

11 mos

Current Experience

Surveymonkey

Technical Intern

Jul 2025 – Present · 11 mos · Bengaluru, Karnataka, India · On-site

1. Contributed to the modernization of SurveyMonkey’s global billing & payments platform (>$1B annual revenue, 205+ countries) by implementing Stripe/ProcessOut–powered microservices, improving scalability, compliance, and reliability.
2. Developed and maintained payment workflows with tokenized invoice handling, enabling seamless, compliant invoice processing at SaaS scale.
3. Built and supported a Kafka-based event pipeline for usage-based pricing (RBP), ensuring reliable, auditable revenue allocation through structured events and retry mechanisms.
4. Implemented refund and dispute handling logic aligned with Stripe events, enabling accurate proration and reducing potential revenue leakage.
5. Delivered cross-team features such as a Promo Code Observability API and enhancements to the Billing Cycle API, improving reporting, analytics, and operational visibility.
6. Updated Python version across five major microservices from <3.10 to 3.12, enhancing performance, security, and long-term maintainability.

StripeMicroservicesKafkaPythonAWSBackend Development

Syfe

Back End Developer

Feb 2025 – May 2025 · 3 mos · Singapore · Remote

1.Refactored legacy error handling in tick price CSV uploads using a centralized GlobalErrorHandler, eliminating recurring pager alerts.
2.Enhanced the file upload experience by upgrading both backend and Securities UI for downloadable success/error logs and better trade dealing traceability.
3.Resolved bank account prefix mismatches, improving data integrity and enabling accurate manual deposit linking by Ops teams.
4.Improved portfolio UX by displaying FX trade wallet balances to prevent premature submissions and enabling real-time rebate rate editing with audit logging and cache syncing.
5.Redesigned legacy audit logs to capture both old and new values for key updates, streamlining auditing and eliminating reliance on database-level queries.

KotlinError HandlingUI DevelopmentBackend DevelopmentUser Experience

Grip invest

Full Stack Engineer

Jul 2024 – Dec 2024 · 5 mos · Gurugram, Haryana, India · Remote

1. Updated the GRIP’sandbox platform with new updated APIs, enhancing visibility of request and response interactions for external companies, and added a redirection section.
2. Created complete API documentation ensuring clarity, consistency, and ease of use for developers and stakeholders.
3. Implemented additional frontend features to support key functionalities within the Grip-Terminal-Web (Internal tool).
4. Developed a dynamic internal tool leveraging Jira data for productivity tracking across QA, App, and Product Tech teams, featuring real-time dashboards to monitor issue types. Implemented custom date filters for analysis beyond sprint cycles and designed a database to streamline productivity calculations, tracking working days, leaves, and employee details.
a. Sprint-wise Productivity Tracking: Select a sprint to track Adhocs, Incidents, Unassigned Tasks, Bugs, and developer productivity, factoring in working days, leaves, and story points assigned based on developer availability.
b. Sprint Summary: Real-time metrics including:
Completed Story Points: Tracks total completed story points.
Planned Tasks: Tracks tasks initially planned for the sprint.
Bugs & Incidents: Monitors bugs and incidents reported.
Urgent Issues: Highlights critical issues.
Tickets Added Post-Sprint Start: Identifies tickets added after the sprint begins.
c. QA Productivity Dashboard: Tracks bugs, false bugs, and incidents categorized by root cause (operational, third-party, coding-related).
d. Week-on-Week Tracking: Tracks metrics (Monday to Friday) with customizable date ranges. Metrics include incidents, adhocs, and recurring trends for better resource allocation.
e. App Development Team Productivity: Tracks developer productivity by factoring in working days, leave days, and expected story points, giving managers insights into individual and team performance.

API DevelopmentJiraFrontend DevelopmentFull Stack Development

Homelane

Software Engineer

May 2024 – Jul 2024 · 2 mos · Bengaluru, Karnataka, India · Hybrid

Automated the floor plan drawing process, reducing manual entry time from 15-20 minutes to under a minute, significantly improving efficiency and reducing errors.
Implemented a deep multi-task neural network based on a research paper using PyTorch, NumPy, and Python, incorporating advanced mechanisms to predict room boundaries and types.
Utilized OpenCV for image processing to refine predictions and developed algorithms for accurate room mapping in SpaceCraft(internal tool).

Neural NetworksOpenCVPythonMachine Learning