Feb 2023 – Feb 2025 · 2 yrs · Gurugram, Haryana, India · Hybrid

• "Specialist in Scalable Backend Solutions for Financial Services"
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• ◦ Disbursements: Built a rule engine for routing transactions to multiple banks and automating the disbursement process by integrating external bank APIs, reducing manual intervention to 10%.
• ◦ Co-Lending: Designed and executed co-lending functionality to support multiple lenders, leading to a 30% increase in loan disbursement volume, a 20% reduction in default risk, and 25% faster loan processing.
• ◦ Repayments: Implemented an escrow-based daily repayment system to automate the distribution of funds between lenders and borrowers based on predefined splitting logic, reducing the risk rate by 15%.
• ◦ GPay-Escrow: Integrated Google Pay (GPay) by fetching transaction data from GPay’s SMTP server, calculating deductions for over 10,000 borrowers per day, and efficiently receiving the deducted amounts.
• ◦ Monitoring: Deployed New Relic for real-time microservice monitoring, tracking failures and resource usage, reducing downtime by 30% and improving incident response time by 25% through automated email alerts.

Jun 2022 – Jan 2023 · 7 mos · Bengaluru, Karnataka, India · On-site

• "Specialist in Scalable Backend Solutions and Efficient Data Pipeline Architectures"
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• ◦ Anomaly Detection: Developed a comprehensive solution for real-time anomaly detection that leveraged Kafka and Apache Flink being utilized by over 15 data engineers, facilitating rapid response to data irregularities.
• ◦ SIM Activation: Automated SIM activation for Ola scooters by integrating Jio’s SIM activation and status APIs, reducing manual work to less than 5% and supporting the activation of over 20,000 SIMs per month.
• ◦ Promise Engine: Designed a promise-engine service for efficient vehicle ordering and inventory management, enabling the sale of over 20,000 scooters monthly through seamless integration with the inventory system.

May 2021 – Aug 2021 · 3 mos · Kanpur, Uttar Pradesh, India · On-site

• ◦ Predictive Modeling: Constructed a machine learning model to optimize compositions of Perovskite solar cells, boosting performance by over 40% and achieving accuracy exceeding 85%.
• ◦ Dataset Construction: Compiled and processed a dataset from research papers, performing regression to predict stability, and analyzing correlations to identify key variables, significantly improving predictive accuracy.
• ◦ Correlation: Applied Spearman’s rank order correlation method to determine the most influential cation on stability, with Cs resulting in a stability value of 740 hours with an optimized composition.