About
Software Engineer with hands-on experience building scalable, distributed backend services in Java and Python on the Google Pay ecosystem, using microservice patterns, gRPC, and REST APIs. Delivered 20% higher data pipeline throughput, 10% faster data preparation, and 5% lower request latency across 1M+ daily requests. Modernized 40+ gRPC microservices within 3 months, resolved P0/P1 incidents, and collaborated cross-functionally in Agile teams. Strong foundation in Core/Enterprise Java, Spring Boot, REST/Microservice architecture, SQL, and cloud (GCP).
Skills & Expertise (39)
Work Experience
Software Engineer Apprentice
Mar 2025 - Mar 2026
Engineered backend features and scalable distributed services for the Google Pay ecosystem using Java, Python, and SQL within an Agile development environment, following enterprise software design and testing practices. Modernized 40+ legacy gRPC microservices within 3 months by updating Protocol Buffer schemas and service logic, ensuring backward compatibility across dependent distributed systems โ direct experience in service migration and REST/RPC-based microservice architecture. Improved data pipeline throughput by 20% through workflow simplification, code refactoring, and added unit test coverage, strengthening scalability and reliability for downstream analytics and ML processing. Modernized DNN-based merchant analytics pipelines by introducing lightweight data models and streamlining business logic, reducing data preparation time by 10% and request latency by 5% across 1M+ daily requests. Implemented secure, DMA-compliant data handling workflows with cross-functional teams, applying data validation and access-control practices to strengthen user privacy and regulatory compliance. Executed staged production deployments and triaged/resolved critical P0/P1 incidents under time pressure; authored technical documentation for system architecture, improving onboarding and long-term maintainability.
Software Engineer Intern
Samsung Electro-Mechanics
Feb 2024 - Jul 2024
Developed C# and Python-based desktop applications using WPF and MVVM, improving AI-driven defect detection accuracy from 98.5% to 99.2% through image processing and annotation workflows. Built and enhanced enterprise desktop applications, reducing attendance management time by 50% and improving issue resolution turnaround by 35%.
Education
B.Tech, Computer Science - B.M.S. Institute of Technology & Management
2020 - 2024 ยท Afghanistan
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Citizen
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Open to Relocation
Skills (39)
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