V. Kumar — Software EngineerBengaluru, IN
● Available for opportunities

Varun
Kumar K

Software Engineer building systems that stay up under load.

2+ years designing and scaling distributed microservices — modernizing monolithic systems, wiring AI into production applications, and building the observability and automated testing that makes the whole thing provable. Today that runs at 100M+ patients evaluated daily.

Selected outcomesFig. 01
0M+
patients evaluated daily
distributed targeting platform
0%
latency reduced
monolith → microservices
0%
classification accuracy
AI response interpretation
0%
throughput gained
Postgres partitioning
0%+
unit test coverage
up from 30%
0%
release success rate
automated test framework
01

Overview

I'm a backend engineer with 2+ years designing and scaling distributed microservices that handle high-volume, multi-channel communication across text, email, and fax. Most of my time goes into taking things that were built to just work and making them work at scale — splitting them apart, measuring them, and making failure loud instead of silent.

The thread through my work is reliability you can prove: a monolith becomes a set of focused services, an inbox of freeform replies becomes structured signal an AI can act on, and a black-box deploy becomes a dashboard with alerts. I like the part of engineering where a graph tells you the truth.

Education
B.E., Computer & Communication Engineering
NMAM Institute of Technology (NMAMIT), Nitte
2020 — 2024CGPA 8.25/10
02

Experience

Aug 2024 — Present
Outcomes
Bengaluru, India
● Current

Software Engineer I

  1. 01Led the migration of a large monolith handling text, email, and fax channels into a channel-specific distributed microservice architecture (Java, Spring Boot) — cutting latency by 60% and improving maintainability and monitoring.
  2. 02Own a distributed, microservice-based targeting platform evaluating 100M+ patients daily against eligibility rules and publishing decisions to downstream systems.
  3. 03Delivered 10+ revenue-generating programs on the targeting platform, taking each from requirements through production rollout.
  4. 04Engineered an AI-powered classification system interpreting user responses — including adverse events and stop requests — at 99% accuracy, feeding results into downstream automation workflows.
  5. 05Implemented PostgreSQL partitioning on AWS RDS to scale database workloads under growing traffic; led a PostgreSQL version upgrade and a Terraform-based database migration, driving 40% higher throughput.
  6. 06Built an automated testing framework (TestNG) that eliminated manual regression testing and lifted release success to 95%; raised unit test coverage from 30% to 90%+ and added SonarQube quality gates.
  7. 07Instrumented services with Prometheus and Grafana for real-time pattern detection across messaging traffic, adding alerting that cut production monitoring effort from hours to minutes.
May 2024 — Jul 2024
Outcomes
Bengaluru, India

Software Engineer — Intern

  1. 01Built configuration-management REST APIs that let teams update service configurations through endpoints instead of manual database queries, removing the need for direct production database access.
03

Projects

2026 — Present
Relay
● In Progress

Multi-Channel Messaging Gateway

A distributed messaging platform fronted by a centralized Spring gateway that exposes secure, rate-limited REST APIs for SMS, WhatsApp, email, and fax — each channel backed by its own independently deployable microservice. A self-hosted take on the vendor-abstraction platforms I build in production.

  1. 01Centralized Spring gateway exposing secure, rate-limited REST APIs, with ingest, routing, and per-channel delivery split into services that scale and deploy independently.
  2. 02Routing layer dispatches each message to the single channel named on the request; adding a provider is just a new adapter (Strategy pattern), with no change to core routing.
  3. 03Channel-specific delivery workflows with decoupled provider integrations, so a vendor swap never reaches the caller.
  4. 04Kafka pipeline using the transactional-outbox pattern, with retry and failure handling plus duplicate suppression that guarantees at-most-once user-visible delivery.
  5. 05Exponential-backoff retries with a dead-letter queue, per-vendor rate limiting (Redis), and automatic multi-provider failover.
  6. 06Prometheus + Grafana dashboards for per-channel latency and delivery success, runnable locally via Docker Compose.
JavaSpring BootKafkaRedisPostgreSQLDocker
04

Stack

Languages
JavaPythonJavaScriptSQL
Frameworks
Spring BootHibernate / JPAReactFlyway
Cloud & DevOps
AWS (RDS, EC2, S3)KubernetesDockerTerraformArgoCDConcourseCI/CD
Data & Messaging
PostgreSQLMySQLRedisApache KafkaCaching
Architecture
Distributed SystemsMicroservicesRESTful APIsEvent-Driven DesignSystem Design
Testing & Observability
JUnit 5TestNGMockitoSonarQubePrometheusGrafanaKibana
Tools & Practices
GitMavenLinuxAgile / ScrumCode Review
05

Contact

Have a system that needs to scale, or a role that needs an engineer who measures twice?

Open to conversations. The fastest route is email.