At Capital One, I engineered and optimized high-volume transaction processing pipelines, handling 100M+ transactions daily. By redesigning the system architecture and implementing microservices, gRPC, and real-time data pipelines, I increased throughput by 30% and reduced error rates, significantly enhancing system scalability and reliability. Additionally, I developed a location-matching service that enriched transaction data with merchant details, improving the user experience in financial applications.

Tech Stack:

Backend: Java, Node.js

Infrastructure: AWS (EC2, S3, Lambda, DynamoDB, CloudWatch)

Data Processing: Kafka (Airflow in newer systems), Postgres

Architecture: Microservices, gRPC-based communication, CI/CD

At Trackonomy, I led the development of a decentralized file-sharing system with end-to-end encryption, ensuring FedRAMP compliance for secure data transfers. This system was later acquired by the U.S. military for its high-security standards. Additionally, I contributed to IoT-based tracking solutions, integrating tracking chips with real-time data pipelines to enhance asset monitoring and security. I designed a peer-to-peer architecture, implemented encryption protocols, and ensured zero-trust security principles, enabling secure and scalable communication across distributed systems.

Tech Stack:

Backend: Nodejs, Python,

IoT & Tracking: Embedded Systems, RFID, GPS, BLE (Bluetooth Low Energy)

Infrastructure: AWS (EC2, S3, IAM, KMS, VPC)

Security & Encryption: AES-256, RSA, Zero-Trust Architecture

Networking & Storage: P2P Protocols, Blockchain-based Access Control, Distributed File Storage

vent-Driven Architecture, Distributed Systems

At Amazon, I contributed to AI-driven research and development, optimizing large-scale machine learning-based ranking algorithms for personalized recommendations. I built high-volume data pipelines, enabling real-time user behavior analysis and improving recommendation accuracy across e-commerce platforms. I worked closely with AI researchers and data scientists, optimizing ML model performance and scaling real-time inference systems to support millions of users globally.

Tech Stack:

Backend: Java, Python, Node.js

Infrastructure: AWS (S3, Lambda, SageMaker, DynamoDB, CloudWatch)

ML & Data Processing: TensorFlow, PyTorch, Spark, Airflow

Architecture: Microservices, Event-Driven Architecture, Distributed Systems