Featured
Payment Settlement Engine
A settlement layer built so that failure is expected, not exceptional — every step can retry without the customer ever paying twice.
Backend Engineer·Java · Spring · Kafka
Open case studybackenddistributed
The catalogueEngineering case studies,
Engineering case studies,
browse like films.
Each project is a full case study — the problem, the architecture, and the decisions that made it hold. Shipped work and system-design concepts, clearly marked.
Browse the catalogue
Recently shipped
Real work1
Rate Limiting Service
One gate in front of everything
Backend Engineer·Redis · Java · Docker
ProblemAbusive traffic reaches services that matter — unless something stops it at the edge.
SolutionA shared Redis sliding-window limiter with policy in one place.
ImpactBad traffic is turned away at the perimeter before it costs a real service anything.
2
Job Scheduler
Background work that actually finishes
Backend Engineer·Java · PostgreSQL · Redis
ProblemA worker dies mid-task — and something important is quietly lost.
SolutionA distributed scheduler with retry backoff and dead-letter handling.
ImpactThe ugly case everyone forgets — a crash mid-job — is the case it's built for.
3
LLM Gateway Service
AI treated like any other dependency
Backend Engineer·Python · Redis · OpenAI
ProblemThe model has a bad day — and the whole product feels it.
SolutionA gateway with streaming, token budgeting, caching, and fallbacks.
ImpactThe rest of the product never has to know the AI is struggling.
Concepts & explorations
System designHow I'd approach classic problems — designs and explorations, not shipped products. Marked as concepts so nothing here pretends to be more than it is.
Concept
Distributed Notification Platform
One event, many channels, exactly once
System design·Kafka · Redis · Workers
ProblemOne event has to reach email, push, and SMS — without double-sending.
SolutionFan-out workers per channel with a dedupe key and per-user preferences.
ImpactA retry re-delivers safely; a channel outage never blocks the others.
Concept
RAG Knowledge Search
Answers grounded in your own documents
System design·Embeddings · Vector DB · LLM
ProblemAn LLM answering from its own memory invents facts about your docs.
SolutionRetrieve the relevant passages first, then let the model answer only from them.
ImpactAnswers stay grounded in real sources, with citations back to the passage.
Concept
Feature Flag Service
Ship dark, roll out slow, kill fast
System design·Rules · Redis · SDK
ProblemA risky feature needs a kill switch that works in seconds, not a redeploy.
SolutionCentral rules evaluated at the edge, cached, and pushed to SDKs live.
ImpactRoll out to 1% then 100% — or turn it off instantly — without shipping code.
Concept
Real-time Analytics Pipeline
From raw events to live dashboards
System design·Kafka · Stream · OLAP
ProblemDashboards need fresh numbers, but re-querying raw events every time is too slow.
SolutionAggregate events in a streaming layer and serve pre-rolled metrics.
ImpactDashboards read pre-aggregated rollups instead of scanning raw history.
Concept
API Gateway Platform
One front door for many services
System design·Routing · Auth · Limits
ProblemEvery service re-implementing auth, limits, and logging is duplicated risk.
SolutionA gateway that owns cross-cutting concerns once, in front of everything.
ImpactServices focus on their domain; the edge handles the shared machinery.
Concept
Log Aggregation & Search
Find the one line that explains the outage
System design·Ship · Index · Query
ProblemLogs scattered across a hundred machines are useless during an incident.
SolutionShip every log to one place, index it, and make it searchable in seconds.
ImpactDuring an outage, the answer is one query away instead of a hundred SSH sessions.
Experience
Three seasons of building things that had to stay up.
Own the architecture of a high-performance product platform and the experience layer users actually feel. The work splits across two fronts: driving down perceived latency so the interface responds instantly, and building the internal leverage — a modular design foundation, controlled experimentation, and automated delivery — that lets the product evolve quickly without regressing.
01
Problem
The product's interface carried too much weight on load, and every new capability slowed the development lifecycle as complexity compounded.
Solution
Re-architected the experience layer around a modular, composable component foundation that cut time-to-interactive and shortened the feedback loop for every change that followed.
02
Problem
Product decisions were being made without a dependable way to measure their effect on how new users onboard.
Solution
Designed a controlled experimentation capability so features are validated against real onboarding behavior before they ship, reducing drop-off.
Education
The theory underneath the systems.
George Mason University
M.S., Computer Science
Fairfax, VA
Jan 2024Dec 2025
Completed
Coursework
Distributed SystemsSystem DesignDatabasesOperating SystemsSoftware EngineeringAlgorithmsData Structures
The Lab
Experiments you can actually run.
Small, honest demos of the patterns behind the work — a token-bucket limiter, exponential backoff, a cache — running right here in your browser. Plus the deeper case studies they come from.
About
I build for the day it breaks — not the day it demos.
Most software works on the happy path. I spend my time on the other one — the 3 a.m. spike, the retry storm, the dependency having a bad day. The systems I'm proud of aren't the ones that never fail; they're the ones that fail in ways you can see, contain, and recover from.
That turns into a way of working. I start from the failure and design backward, so the good day takes care of itself. I'd rather ship something simple and correct than clever and occasionally wrong. And I try to leave every system more legible than I found it — explicit states, honest names, a path you can actually trace — so the next engineer can move without fear.
How I work01 / 05
Principle 01
Design from the failure backward
Start at what breaks under load, then work back to the architecture. The happy path takes care of itself.
Let's build something that stays calm when traffic doesn't.
Vamsi MaramOpen to opportunities