Backend SDE

Distributed Systems & Backend Engineering

Four years building Java services that handle millions of records daily. Now applying that systems thinking to ML infrastructure, because production AI needs production engineering.

About This Role

Backend engineering is where I started. At TCS I owned Java-based distributed data processing services handling millions of records daily, reduced processing latency by 40% through service refactoring, and led system design reviews for data-intensive microservices. That foundation of writing systems that don't break under load directly informs how I build ML infrastructure today, inference endpoints, retraining pipelines, and real-time clinical AI systems all benefit from the same principles: fault tolerance, observability, and graceful degradation.

LeetCode Progress

722

Problems Solved

Easy: 293

Medium: 350

Hard: 79

1,661 submissions 46 day max streak 226 active days

Backend Projects

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Experience

Tata Consultancy Services

Senior Software Engineer → Software Engineer

Jun 2019 - May 2023 · 4 years · Chennai

Designed and owned Java-based distributed data processing services handling millions of records daily across production systems. Led system design and architecture reviews for data-intensive microservices. Built fault-tolerant backend pipelines with high availability and reduced processing latency by 40% through service refactoring and caching. Stack: Java, Spring Boot, Microservices, REST APIs, SQL.

Qure.ai Technologies

AI Solutions Engineer Intern

Mar 2026 - May 2026

Built and maintained backend infrastructure for clinical AI systems: REST API endpoints, data extraction pipelines for radiologist report parsing and EMR data across EPIC/FHIR-integrated hospital networks. Ensured 99%+ uptime across 6+ live health system sites under real-time SLAs.