Soumyadip Sarkar

Software Engineer

Bryan, Texas, United States3 yrs 6 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Designed microservices handling 30K+ RPS.
Scaled platforms to 600M+ DAU.
Improved ad revenue by 3× and cut infra costs by 50%.

Stackforce AI infers this person is a Backend Engineer specializing in high-scale distributed systems for B2C applications.

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Skills

Core Skills

JavaSpring BootGolangKafkaRedisPrometheus

Other Skills

AJAXApache MesosBig QueryBig TableCC++Cascading Style Sheets (CSS)CassandraCloudSQLConvolutional Neural Networks (CNN)DockerFastAPIGCPGitGitHub

About

Software Engineer with experience building high-scale distributed systems across Salesforce, Flipkart Health+, ShareChat, and Myntra. Designed microservices handling 30K+ RPS and scaled platforms to 600M+ DAU. Improved ad revenue by 3×, cut infra costs by 50%, reduced latency from 250ms → <10ms, and support delays from 1 hr → <1 sec. Skilled in Java, GoLang, Spring Boot, Kafka, Redis, GCP, and Kubernetes. Passionate about backend, infrastructure, and platform engineering roles where I can drive performance optimization and system scalability. Currently pursuing a Master’s in CS at Texas A&M University (Aug ’25 – May ’27) and actively seeking Summer 2026 internships

Experience

3 yrs 6 mos

Total Experience

1 yr 2 mos

Average Tenure

Current Experience

Salesforce

Software Engineer

Jan 2024 – Aug 2025 · 1 yr 7 mos

Designed and implemented a milestone action system processing 15M+ daily field updates, with distributed queueing and record-level locking for reliable concurrent updates.
Migrated UI modules to web components, improving load speed by 35% and reducing JavaScript errors by 65%
Tech Stack: Java, Spring Boot, MySQL, Kafka, Redis, Docker, Kubernetes, REST APIs, OpenAPI, JUnit, Git, Jenkins

JavaSpring BootMySQLKafkaRedisDocker+6

Flipkart

Software Engineer

Jun 2023 – Dec 2023 · 6 mos

Sharechat

Software Engineer

Jan 2022 – Jun 2023 · 1 yr 5 mos

Designed and built a revenue generating service named Ads Network Service which can handle over 30,000 requests
per second with nearly 600 million users
Reduced infrastructure cost by 50%, cut latency from 250 ms to under 10 ms, and optimized resource usage by reducing
pod count from 600 to 250.
Developed a real-time event processor using Kafka and Java to handle high-throughput publisher integrations.
Implemented a distributed Redis-based API rate limiter to guarantee system availability under peak traffic.
Built backend modules for publisher onboarding and real-time analytics, enabling faster adoption of the Ads Network.
Set up centralized monitoring and alerting with Prometheus and Grafana, reducing incident resolution time by 60%.
Tech Stack: Golang, Java, Python, Node.js, Kafka, Redis, MongoDB, Cassandra, Prometheus, Grafana, Docker, Kubernetes,
GCP, OpenAPI, Swagger, Memcached