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Aman Gautam

[ Portfolio 2026 ]

Software engineer

Backend & distributed systems

AmanGautam

I build scalable backend systems: event-driven services, real-time sync and networks that keep working when the internet doesn't.

Software Engineering Intern at DragonX Systems

[01]About

About Aman Gautam

I like to build. Backend systems that stay fast, consistent and honest under load: event-driven services, real-time collaboration and mesh networks that keep talking when the towers go down. Two thousand solved problems taught me to think in trade-offs. Merged pull requests in production open source taught me to ship them.

Based in
India
Studying
B.Tech Computer Science, class of 2027
Focus
Backend and distributed systems
Status
Open to opportunities
2000+

Problems solved

2102

Peak LeetCode rating

22

Merged pull requests

9

Open-source projects

[02]Selected work

Built to hold up

Backend systems for offline mesh messaging, real-time collaboration, distributed chat and fast URL redirection. Each one is a case study.

[03]Experience

Internship · On-site / Hybrid

DragonX Systems

Software Engineering Intern

Jul 2026 – Present

Building GPU-accelerated ASIC physical-design infrastructure: CUDA components integrated into the OpenLane and OpenROAD EDA toolchain.

  • End-to-end ownership of CUDA kernels, OpenROAD integration, build infrastructure, benchmarking, profiling and QoR validation.
  • Built a CUDA-accelerated OpenROAD timing-repair flow single-handedly, with GPU gate sizing, buffering and OpenSTA.
15.7×

Faster GCD timing repair

62%

Faster Ibex GPU timing repair

  • C++
  • CUDA
  • Docker
  • OpenROAD
  • OpenLane
  • Yosys
  • ASIC design automation

Education

Galgotias College of Engineering & Technology

B.Tech, Computer Science

2023 – 2027

Merit-based scholarship with a full tuition waiver for the top admission rank.

[04]Open source
  • Gradle
  • Spring Kafka
  • Apache RocketMQ
  • OpenRewrite
  • Kubernetes Java Client
  • Armeria
  • cBioPortal
  • InsForge
  • JabRef
[05]Competitive programming

Trade-offs, trained daily

It started with DSA and contests. Thousands of problems later, choosing the right data structure under a time limit is a reflex.

2000+

Problems solved across platforms

2102

Peak LeetCode rating, Knight, top 2%

1434

Peak Codeforces rating, Specialist

41

Rated contests

[06]Capabilities

The toolkit

Backend & system design

  • Spring Boot01
  • REST APIs02
  • Microservices03
  • WebSockets04
  • Hibernate / JPA05
  • API design06
  • Caching07
  • Database design08

DSA & problem solving

  • Dynamic programming01
  • Graph algorithms02
  • Tree traversals03
  • Greedy algorithms04
  • Binary search05
  • Sliding window06
  • Two pointers07
  • Backtracking08

Tools & infrastructure

  • Docker01
  • GitHub Actions02
  • AWS EC203
  • Kafka04
  • Redis05
  • PostgreSQL06
  • MySQL07
  • Milvus08
  • Swagger / OpenAPI09
  • k610
  • Render11
  • Netlify12

Languages

  • Java01
  • C++02
  • SQL03

Core stack: Java, Spring Boot, Kafka, Redis, PostgreSQL, Docker, C++, CUDA, WebSockets, Kubernetes, MySQL

[07]Process

Five habits behind every system

A systematic way into hard problems, each habit grounded in a project it shaped.

Step 01 / 05

Deep dive into requirements

Break the problem into its core components. Find the edge cases, the constraints and the scale it has to survive, and ask the clarifying questions before writing a line of code.

Real example

ConvoMesh separates connection handling, routing, caching and persistence, so message delivery scales without tight coupling.

  • Requirements analysis
  • Edge cases
  • Constraints
Step 02 / 05

Design for failure first

Assume the network, the node or the process will die, and decide up front what the system should say when it does. Honest states beat optimistic ones.

Real example

ResQNet persists every envelope before acknowledging it, and reports an undelivered message as expired, never as delivered.

  • Fault tolerance
  • Delivery semantics
  • Store and forward
Step 03 / 05

Scale out, not up

Partition the work so any node can be added or lost. Route by key, share state through a broker and keep services stateless wherever possible.

Real example

The Sync Engine routes every document through a consistent hash ring to one owner node, and keeps three nodes in step over Redis Pub/Sub.

  • Consistent hashing
  • Pub/Sub
  • Horizontal scaling
Step 04 / 05

Make state recoverable

Treat the log as the source of truth. When every change is an event, any node can rebuild exactly what it lost.

Real example

Kafka event sourcing plus snapshots: load snapshot 100, replay events 101 to 104, and the document is back at version 104.

  • Event sourcing
  • Snapshots
  • Replay
Step 05 / 05

Measure, then optimize

Benchmark before believing anything. Profile, find the real bottleneck, change one thing, then measure again.

Real example

Profiling-led CUDA work at DragonX Systems made GCD timing repair 15.7× faster, and load tests held 118 ms average latency at 500 concurrent editors.

  • Benchmarking
  • Profiling
  • Load testing
[08]Recognition

Along the way

  • 2026Hackathon

    Winner, Techfest Hackathon

    Smart-Sagar, an AI-powered maritime solution.

  • Top 2%Competitive

    LeetCode Knight

    Max rating 2102, in the top 2% of contestants.

  • 1434Competitive

    Codeforces Specialist

    Max rating 1434 across rated rounds.

  • 22 PRsOpen source

    Open-source contributor

    22 merged pull requests across Gradle, Spring Kafka, Apache RocketMQ, Armeria, OpenRewrite and more.

  • 2023Academic

    Merit-based scholarship

    Full tuition waiver for the top admission rank at Galgotias College.

  • 2025Leadership

    Event Manager, Galgotias International MUN

    Ran a Model United Nations conference for 200+ delegates, Nov to Dec 2025.

[09]Contact

Let'sbuild

Currently interning at DragonX Systems and open to backend, distributed systems and infrastructure roles. Have a role, a system or a hard problem? Send it over.

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