Adarsh Bora

Software Engineer

Pune, Maharashtra, India2 yrs 9 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Reduced latency by 28% in core systems.
Doubled pipeline throughput using Kafka.
Ranked in Top 4% globally on Leetcode.

Stackforce AI infers this person is a Backend Engineer specializing in Fintech and SaaS solutions.

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Skills

Core Skills

Backend EngineeringDistributed SystemsFull-stack DevelopmentComputer Vision

Other Skills

SQL tuningcache-aside patternsasync workflowsKafkaRedisAWSJVM optimizationAgile methodologiesconsumer groupsretriesDLQscache-asideTTL shardingSpring BootHikariCP

About

I’m a Software Engineer specializing in backend engineering, distributed systems, and high-performance microservices. At TCS, I work on large-scale insurance platforms where I’ve optimized core systems, reduced latency by 28%, doubled pipeline throughput using Kafka, and implemented Redis caching layers that cut database load by 30%. I enjoy working on systems that require scalability, reliability, and deep performance tuning—from JVM optimization and concurrency (Executors, Futures, CompletableFuture) to cloud deployments on AWS (EC2, RDS, S3, CloudWatch). I actively participate in competitive programming and system design, ranked in the Top 4% globally on Leetcode (Contest Global Rank #1355, 1800+ Rating). I’m also a TCS AI Hackathon Finalist and ranked #2 in the Vodafone Idea Innovation Hackathon. Previously at Bootprime Digital Solutions, I built secure, production-ready APIs, implemented JWT-based authentication, created analytics dashboards, containerized services with Docker, and improved observability through centralized logging and metrics.

Experience

2 yrs 9 mos

Total Experience

1 yr 4 mos

Average Tenure

1 yr 5 mos

Current Experience

Tata consultancy services

System Engineer

Jan 2025 – Present · 1 yr 5 mos · On-site

Owned performance optimization of a core claims processing microservice in a large-scale insurance platform, reducing P99 latency by 28% through SQL tuning, cache-aside patterns, and async workflows.
Redesigned a high-throughput claims pipeline using Kafka (partitioning, consumer groups, retries, DLQs), enabling 2x peak traffic handling with stable throughput and at-least-once delivery guarantees.
Architeched and implemented Redis caching layers (cache-aside with TTL sharding), reducing PostgreSQL read load by 30% and improving API response times across multiple regions.
Improved service stability by tuning thread pools, async execution (CompletableFuture), and JVM GC parameters, reducing latency spikes and thread starvation under load by 25%.
Deployed backend services and deployed them on AWS EC2 with RDS, integrating CloudWatch for logs and metrics and reducing on-call debugging time by 35%.
Embedded an internal LLM-based log analysis tool (RAG + vector search) to accelerate root-cause analysis of production incidents, improving developer productivity by 35%.
Collaborated with cross-functional teams in Agile environments, contributing to code reviews, design discussions, and CI/CD improvements using Jenkins.

SQL tuningcache-aside patternsasync workflowsKafkaRedisAWS+4

Bootprime digital solutions

Full-stack Developer

Aug 2023 – Dec 2024 · 1 yr 4 mos · Pune, Maharashtra, India

Engineered and optimized Spring Boot REST APIs serving 1,000+ DAU, implementing connection pooling (HikariCP), request validation, and Redis caching for higher throughput.
Enhanced secure authentication and authorization using Spring Security + JWT with role-based access control and stateless sessions, validated through unit and integration tests using JUnit/Mockito.
Containerized backend and frontend services using Docker reducing deployment time by 60%.
Improved system observability by implementing centralized logging, metrics dashboards, and health checks, reducing mean time to detection (MTTD) of performance issues.

Spring BootHikariCPSpring SecurityJWTDockerJUnit+3

Mogo esports pvt ltd

Esports Campus Ambassador

Nov 2022 – Jan 2023 · 2 mos · Pune, Maharashtra, India

Vodafone idea limited

Project Intern

Jul 2022 – Aug 2022 · 1 mo · WFH

Designed and implemented a real-time digital parking system using OpenCV to automate detection of available
and occupied parking spots from live video feeds, improving search efficiency by 30%.
Developed robust computer vision workflows: converted frames to grayscale, reduced noise via Gaussian blur, and
utilized Canny edge detection and contour analysis to reliably identify parking boundaries under varied lighting
conditions.
Devised scalable slot detection logic by comparing slot region pixel intensities, ensuring accurate and consistent
identification of occupancy status.
Integrated intuitive visual cues, overlaying color-coded rectangles (green for available, red for occupied) on the
video stream, enhancing user experience and spot-finding speed.