Jan 2025 – Jul 2025 · 6 mos · Hyderabad, Telangana, India · On-site

• Loyalty Vendor Dashboard:
• Designed, developed and deployed a real-time monitoring dashboard using Next.js (TypeScript, Tailwind CSS) for the frontend and Spring Boot (Reactive) REST APIs for the backend, enabling seamless integration and real-time visibility into critical loyalty service metrics.
• Integrated New Relic (NRQL) for observability, PagerDuty for incident management, and implemented an ARIMA model for incident trend forecasting.
• Deployed to Azure AKS, with CI/CD via GitHub Actions.
• Functional Test Suite – Multi-Tender Loyalty (MTL):
• Developed Gherkin-based UATs for validating store transactions.
• Implemented XML/JSON handling and assertions using Java.
• Cost Optimization – Continuous Delivery Platform:
• Resized infrastructure resources to boost utilization from 5% to 60%, significantly reducing cloud waste.
• Designed and implemented automated weekend snooze for Dev/Non-Prod environments.
• Delivered ~$13K/year in total savings across 50+ services/consumers.
• Ecom Enrollment Delay Analysis:
• Investigated service-level latencies and identified race conditions in mobile tier progression.
• Analyzed timestamps/logs across microservices and delivered a detailed resolution report.
• New Relic Integration for Loyalty Services:
• Developed and deployed custom New Relic Agents across multiple services to capture key performance metrics.
• Optimized high-volume NRQL queries using FACET, significantly reducing memory overhead and enabling scalable real-time observability across multiple high-throughput services.
• Innovation Presentation – HDC Prayogshala 2.0:
• Presented the Loyalty Vendor Dashboard in an internal innovation forum, demonstrating its impact on service reliability, incident visibility, resolution times and vendor level observability.

Dec 2022 – Dec 2023 · 1 yr · Visakhapatnam, Andhra Pradesh, India · Remote

• Led cross-functional team of 5 interns in Agile environment to deliver deep learning defect detection system using YOLO, OpenCV, and PyTorch
• Achieved 2x throughput improvement and 40% reduction in false negatives through synthetic data augmentation
• Built scalable backend APIs for edge alerting on AWS (EC2, S3, SageMaker) with sub-200ms inference latency
• Developed full-stack GPS tracking system with geofencing capabilities, improving dispatch accuracy by 25%