Jun 2021 – Jun 2025 · 4 yrs · Hyderabad, Telangana, India · Hybrid

• ◦ Architected and led the development of DataUpgrade Service, overseeing a team. This service migrates on-premises AX2012 databases to Dynamics 365, a cloud-based ERP and CRM solution. Designed the flow, driving cloud migrations and contributing $300M in revenue to Microsoft.
• ◦ Designed and implemented Shadow Copy for AX2012 to Dynamics 365 migrations, optimizing upgrade speed by 80% for large tables and complex schemas. This initiative cut ICM volume by 50% and reduced support cases by 60%.
• ◦ Fully automated the Azure database tier for special customers, eliminating the need for customers to manually raise tickets and involve customer support, reducing support costs by $30M and enhancing customer satisfaction.
• ◦ Customer Reliability Improvement: Owner and Primary Contact for Migration Flow of D365 Reduced
• incident volume from 60 in 2021 during post-development to 0-1 per month during maintenance.
• ◦ Daily Tasks: Improving service latency, Modern C++ development, bug fixes, code reviews, design discussions, and DRI Support.

Aug 2020 – Mar 2021 · 7 mos · Remote

• ◦ Independently developed a profitable intraday breakout strategy for Nifty 100 stocks and futures, as well as for indices like Nifty 50 and Nifty Bank, achieving 91% profitability across the Nifty 100 stocks using standardized parameters to avoid overfitting
• ◦ Backtested over an 8-year period (2013–2020), including the March 2020 COVID crash, achieving a total return of 832% (CAR 30%) on Nifty 50, outperforming the Nifty 50 index by 20%. Key metrics: CAR/MDD - 1.9, Win rate - 43%, Risk/Reward - 1.6.
• ◦ Deployed the strategy in a live market with a test capital of 10L Rs, created a ranking system to identify top stock futures with a max open position of 5. Traded Nifty 100 stock futures and Nifty 50 futures, achieving annualized returns of 36% and 48%, respectively, based on 2 months of trading.

May 2020 – Jul 2020 · 2 mos · Bengaluru, Karnataka, India · Remote

• ◦ Tracking & Recommendation Service: Single-handedly developed an Android service to track user activity
• and recommend personalized solutions using Bixby§ AI.
• ◦ Latency Reduction: Enhanced the existing tree data structure for the accessibility service to a Trie data
• structure, enabling efficient user data collection for training machine learning models in Bixby§ AI. Improved
• Android UI responsiveness, reducing latency to under 50ms, and optimized storage space usage by 90%.
• ◦ Data Analysis: Owned the process of transforming user activity data from an Android service into labeled data,
• which was then supplied to the test AI model for training, improving its F1 score by 9% during evaluation.