Software Engineer II at MSCI · Pune, India

Rushikesh Godase

Backend engineer for systems that have to scale.

4.5 years building Java and Spring Boot microservices and distributed systems across FinTech, analytics, real-time messaging, conversational AI, automotive retail and customer data platforms. I take systems from design to production on AWS and Kubernetes, and I work AI-natively: Claude Code, Cursor and agents are part of how I design, build and test every day.

Email me View resume See the work LinkedIn GitHub LeetCode Medium

producerspartitioned topicsconsumers

0events since you opened this page, at the 260M/day pace of the pipeline I worked on at Gupshup

Industries

Six industries, one backend skillset

Swipe through the businesses I have built for.

1 / 6

MSCI

−80%P95 latency, 5s to under 1s

FinTech and wealth analytics

Risk and portfolio analytics, financial securities search and usage reporting.

Java 17OracleSolr

Gupshup

260Mevents processed per day

Real-time messaging

Event generation and a 6,000 TPS messaging exit point with full observability.

KafkaJavaAWS

Gupshup

40Mrecords ingested per minute

Conversational AI

Campaign Manager on a conversational cloud: clone, recurring and heartbeat campaigns.

KafkaPostgreSQLMongoDB

Gupshup

+40%read throughput

Customer data platforms

Customer-profile microservices, secured APIs and fast reads for personalization.

RedisMongoDBSpring Security

Tekion

F&Ifinance and insurance

Automotive retail

Dealership financing and insurance workflows on the Automotive Retail Cloud.

JavaSpring Boot

Gupshup

99.99%availability

Enterprise B2B SaaS

Disaster recovery, production monitoring and an onboarding service for enterprise customers.

AWS RDSPostgreSQLKubernetes

Architecture

The kinds of systems I design and run

Distributed systems

Services that scale out across nodes and keep working when one fails.

Gupshup · MSCI

Event-driven

Producers and consumers decoupled through partitioned Kafka topics, with failure detection and re-trigger.

Gupshup

Multi-tenant

One platform serving many customers, with isolated data and repeatable tenant onboarding.

MSCI · Gupshup

Microservices

Breaking a monolith into modular services that release independently.

MSCI · Gupshup · Tekion

Data platforms

Ingestion, storage, migration and archival of customer and analytics data across PostgreSQL, MongoDB and S3.

Gupshup · MSCI

High availability

Multi-AZ databases, disaster recovery and production monitoring.

Gupshup

Caching and search

Redis in front of databases for hot reads, Solr for search and autocomplete.

Gupshup · MSCI

API gateway and security

Routing through Spring Cloud Gateway, with JWT, RBAC and encryption.

Gupshup

Batch and stream processing

Streaming reads for large files, scheduled batch sync between stores.

Gupshup · MSCI

AI-native engineering

AI is part of how I build, at every step

I have used AI coding tools daily at MSCI, Tekion and Gupshup. They speed me up; I stay accountable for what ships.

Step 1

Design

Explore options, compare trade-offs and draft HLD and LLD with an AI assistant before writing code.

Step 2

Build and refactor

Read unfamiliar code fast and refactor legacy modules with Claude Code and Cursor.

Step 3

Test

Generate JUnit and Mockito tests, then extend them for the edge cases that matter.

Step 4

Verify

Every AI-assisted change goes through code review and tests before it reaches production.

Tools Claude CodeCursorAI agentsMCPMachine learning (Agrotech)

Case studies

What I changed, and by how much

Pick a project to see the problem, the approach and the measured result.

Experience

Where I have worked

Wealth analytics, automotive retail, conversational messaging and customer data platforms.

Jul 2025 – Present
Current

MSCI

Software Engineer II · Wealth, Risk & Portfolio Management Analytics

Brought search database calls to near zero by building an Apache Solr autocomplete service for financial securities, with a CDC-free batch sync from Oracle.
Modernized a legacy multi-tenant JSP application into modular Spring Boot services on Java 17, releasing module by module with zero production disruption.
Lowered REST API P95 latency by 80%+ (5s to under 1s) and sped up usage reports 60% by rewriting SQL subqueries as CTEs and tuning indexes and stored procedures.
Made tenant onboarding repeatable by provisioning a serverless tenant environment on GCP with Terraform.
Accelerated delivery across the SDLC with Claude Code and Cursor for design, refactoring and test generation, validating every change in code review and tests.

Day to day

System design, HLD and LLD for new services, with Spring Data JPA and Hibernate on Oracle, an Angular front end, and Splunk and SonarQube in the delivery loop, in Agile sprints.

Jun 2025 – Jul 2025

Tekion Corp

Software Engineer II · Automotive Retail Cloud, Finance & Insurance (F&I)

Worked on Java and Spring Boot backend services in the Finance & Insurance domain, supporting dealership financing and insurance workflows on Tekion's Automotive Retail Cloud platform.
Used AI coding tools for code reading, refactoring and test generation while ramping up on a large codebase.

Jul 2024 – May 2025

Gupshup Technologies

Software Engineer II · Campaign Manager

Raised ingestion throughput 4x to 40M records/min and trimmed CPU and memory use about 60% by replacing in-memory workbook parsing with streaming reads and multithreading over partitioned Kafka topics, with batch-level failure detection and re-trigger.
Achieved 99.99%+ availability by designing PostgreSQL disaster recovery on Amazon RDS Multi-AZ.
Processed 260M events/day by owning the Java event-generation stage and co-developing the 6,000 TPS real-time messaging exit point, with full production observability.
Reduced storage cost by automating archival of millions of MongoDB records to S3 Glacier.

Also shipped

Campaign features used by enterprise customers: clone campaign, recurring campaigns and heartbeat monitoring.
Cross-team data migration, planned and executed with the owning teams.
Log ingestion on AWS EFS and EBS storage, feeding Coralogix and Zabbix monitoring.
Load and memory profiling with JMeter and JProfiler before production releases, with AI agents and MCP tools in the daily workflow.

Jul 2022 – Jun 2024

Gupshup Technologies

Software Engineer I · Personalize, Customer Data Platform

Lifted read throughput about 40% by developing customer-profile microservices on MongoDB and PostgreSQL with Redis serving frequent reads.
Hardened REST APIs with Spring Security, JWT authentication and RBAC, plus AES data encryption.
Automated deployments to Kubernetes on AWS EKS with Helm charts and GitLab CI/CD.
Reached 90%+ code coverage by authoring 400+ JUnit and Mockito unit tests.

Also shipped

API routing and cross-cutting concerns through Spring Cloud Gateway.
An onboarding service for bringing new customers onto the platform.
A custom AWS library shared across services, so teams use S3 and other AWS services one consistent way.
Services built with Docker and Maven, designed with OOP principles and design patterns, and delivered in Scrum sprints.

Jan 2022 – Mar 2022

Crest Data Systems

Software Engineer Intern

Delivered 90%+ accuracy in phishing URL detection by building a Python service on the PhishTank API.

Skillset

Every skill, with the proof behind it

Select a skill to see where I used it and what came out of it.

Writing

I write about the Java I use

Deep dives on concurrency and design patterns, published on Medium.

Dec 2025 · Concurrency

The Horse Race Multithreading Problem in Java

Barrier synchronization with CountDownLatch, CyclicBarrier and Phaser, then winner detection, timeouts and CompletableFuture orchestration.

Read on Medium →

Jun 2025 · Design patterns

Java Singleton Showdown: Reflection vs Enum

How reflection breaks a classic singleton, and why an enum holds up.

Read on Medium →

All articles on Medium

Projects and publication

Earlier work

Built during my degree, before I moved into backend engineering full time.

2022 · Published paper

Agrotech

A web application that helps farmers decide what to grow. It recommends crops and fertilizers from soil data and detects plant disease from leaf images using machine learning.

PythonMachine learningRandom Forest

Read the paper →

2022 · Project