Skills Projects About Education Contact ↓ Download Resume

zeeshan@portfolio ~ bash

$whoami --verbose

Zeeshan Saleem

$ls ./skills --highlight-core

Node.jsTypeScriptExpress.jsPostgreSQLMongoDBRedisRabbitMQDockerAWSSocket.IO

Open to Remote or onsite Opportunities

Lahore, Pakistan

CGPA 3.45 / 4.0

1 Live Production Systems

$ cat ./system_stats.json

50K/sec

events ingested

analytics engine

5K+

concurrent WebSockets

socket.io cluster

production systems

deployed on AWS

3.45/4.0

CGPA

B.Sc. Software Eng.

./view_projects.sh ↓ Download Resume ✉ codewithzezo@gmail.com GitHub ↗LinkedIn ↗

$ ls ./technical-skills --categorised

Technical Skills

Languages

Backend

Databases

Message Queues

DevOps & Cloud

Patterns & Architecture

$ cat ./projects.json | jq

Projects & Systems

AUTH · IDENTITY · SECURITY

User Management System

● LIVE

Clerk-Inspired Identity Platform

View on GitHub →

Production-ready backend identity platform handling authentication, sessions, multi-tenancy, and permissions with enterprise-grade rigour. JWT refresh-token rotation, bcrypt hashing, and server-side revocation close common session-hijacking vulnerabilities.

Key Engineering Decisions

▸Architected RBAC with fine-grained, per-organization permissions and full multi-tenant data isolation — zero cross-tenant data leakage
▸Engineered JWT auth with refresh-token rotation, bcrypt hashing, and server-side revocation — closing common session-hijacking vulnerabilities
▸Built email verification, password-reset, and account-lockout as isolated TypeScript modules with clean interfaces and no business-logic leakage
▸Integrated Redis for distributed session caching and sliding-window rate limiting, reducing DB load
▸Containerised with Docker and deployed to AWS via CI/CD — push-to-main triggers automated test, build, and deploy

Node.jsTypeScriptExpress.jsMongoDBJWTRedisDockerAWS

Token Strategy

Access + Refresh

Auth Pattern

RBAC + Multi-Tenant

Session Cache

Redis

$ git clone ↗

REAL-TIME · HIGH PERFORMANCE · STREAMS

Real-Time Analytics Engine

● IN PROGRESS

High-Throughput Event Pipeline

View on GitHub →

High-throughput data pipeline that ingests, processes, and serves live metrics at scale. Node.js stream processor handles 50,000 data points/sec with backpressure control, serving 5,000+ concurrent authenticated WebSocket connections.

Key Engineering Decisions

▸Scaled Socket.io to 5,000+ concurrent WebSocket connections with authenticated handshakes and connection pooling
▸Built Node.js stream processor handling 50,000 data points/sec with backpressure control to prevent memory overflow
▸Deployed on AWS Auto Scaling Groups — instances scale automatically under traffic spikes, keeping latency flat
▸TimescaleDB hypertables with time_bucket() for sub-second aggregation queries across millions of time-series events
▸Redis Sorted Sets for real-time 60-second rolling window — realtime endpoint never touches the primary DB

Node.jsTypeScriptSocket.ioTimescaleDBRedisRabbitMQDockerAWS

Throughput

50K/sec

Connections

5K+ WS

Scaling

AWS ASG

$ git clone ↗

$ ./about_me.sh --honest

About Me

I am a backend engineer who builds production-grade systems, not just localhost demos. Every project I build is deployed, monitored, and documented with real architectural decisions behind each choice.

My focus is distributed systems — I understand CAP theorem, the Saga pattern, and the Outbox pattern not from textbooks but from implementing them in real projects where a service crash cannot lose a single event.

I believe in understanding the why, not just the how. Every tech choice has a documented reason — why TimescaleDB over plain PostgreSQL for time-series, why Redis Sorted Sets for the rolling window, why orgId-scoped queries for multi-tenancy.

Currently pursuing B.Sc. Software Engineering at Arid Agriculture University (CGPA 3.45/4.0) while building systems at the level most engineers reach after years on the job.

⬡

Systems Thinker

I design for failure modes, not just happy paths. Circuit breakers, DLQs, graceful degradation.

⚡

Ships to Production

Every project has CI/CD, monitoring, and docs — not a localhost demo.

◈

Documents Trade-offs

Every architectural choice has a reason I can explain in an interview.

⌬

Learns by Building

Every concept I study becomes a feature. RabbitMQ → Outbox Pattern → production.

◉

Reads the Internals

DDIA, not just Stack Overflow. I understand how PostgreSQL MVCC works.

▣

Owns Mistakes

I keep notes on real bugs I have hit. Debugging teaches more than tutorials.

$ cat ./education.json

Education

BSSE

● Currently Enrolled · 2024 – 2028

Bachelor of Software Engineering

Arid Agriculture University, Faisalabad, Pakistan

★CGPA: 3.45 / 4.0

⌚2024 – 2028

◎Open to Remote

Self-driven: Building production-grade systems — a Clerk-inspired User Management System and a Real-Time Analytics Engine — while maintaining a 3.45 CGPA. Relevant coursework: Data Structures, Algorithms, Database Systems, Software Architecture. The majority of what I know came from building real systems with real constraints, not just coursework.

$ ./contact.sh --init-connection

Let's Connect

Status

Actively seeking backend / DevOps remote roles

Location

Faisalabad, Punjab, Pakistan — Open to Remote

✉ codewithzezo@gmail.com

Social Links

send-message.sh

$ cat ./projects.json | jq

Projects & Systems

AUTH · IDENTITY · SECURITY

User Management System

● LIVE

Clerk-Inspired Identity Platform

View on GitHub →

Key Engineering Decisions

▸Architected RBAC with fine-grained, per-organization permissions and full multi-tenant data isolation — zero cross-tenant data leakage
▸Engineered JWT auth with refresh-token rotation, bcrypt hashing, and server-side revocation — closing common session-hijacking vulnerabilities
▸Built email verification, password-reset, and account-lockout as isolated TypeScript modules with clean interfaces and no business-logic leakage
▸Integrated Redis for distributed session caching and sliding-window rate limiting, reducing DB load
▸Containerised with Docker and deployed to AWS via CI/CD — push-to-main triggers automated test, build, and deploy

Node.jsTypeScriptExpress.jsMongoDBJWTRedisDockerAWS

Token Strategy

Access + Refresh

Auth Pattern

RBAC + Multi-Tenant

Session Cache

Redis

$ git clone ↗

REAL-TIME · HIGH PERFORMANCE · STREAMS

Real-Time Analytics Engine

● IN PROGRESS

High-Throughput Event Pipeline

View on GitHub →

Key Engineering Decisions

▸Scaled Socket.io to 5,000+ concurrent WebSocket connections with authenticated handshakes and connection pooling
▸Built Node.js stream processor handling 50,000 data points/sec with backpressure control to prevent memory overflow
▸Deployed on AWS Auto Scaling Groups — instances scale automatically under traffic spikes, keeping latency flat
▸TimescaleDB hypertables with time_bucket() for sub-second aggregation queries across millions of time-series events
▸Redis Sorted Sets for real-time 60-second rolling window — realtime endpoint never touches the primary DB

Node.jsTypeScriptSocket.ioTimescaleDBRedisRabbitMQDockerAWS

Throughput

50K/sec

Connections

5K+ WS

Scaling

AWS ASG

$ git clone ↗