~/hassam — bash

$ whoami

Hassam.

> |

7+ years building real-time platforms, distributed systems, and AI-powered products. Tech Lead experienced in shipping production systems at scale — from hospital allocation engines serving 5,000 patients/day to LLM inference pipelines.

GitHub LinkedIn Twitter Resume

$ cat about.md

About Me

I'm Hassam — a Senior Full Stack Engineer and Tech Lead with 7+ years shipping production systems across healthcare, e-commerce, and enterprise SaaS. Currently leading engineering at ProductBox, where I manage a 20-person team across web, mobile, and infrastructure.

My work spans real-time platforms (WebSockets, Redis pub/sub), distributed backend systems (Node.js, FastAPI), and cloud infrastructure (AWS, Pulumi IaC). I've built systems serving 5,000+ patients/day, LLM inference pipelines, and mobile apps with React Native.

I hold a BS in Computer Science (KMK-recognized, equivalent to a German Bachelor's).

Years Experience

10+

Production Systems

Engineers Led

Hospitals Served

$ ls ./skills/

Frontend & Mobile

ReactNext.jsTypeScriptReact NativeGraphQLTailwind CSS

Backend & APIs

Node.jsFastAPIPythonWebSocketsWebRTCREST/GraphQL

Data & Infra

PostgreSQLRedisMongoDBDockerAWS ECS/LambdaPulumi

AI & Systems

LLM InferenceRedis StreamsMicroservicesCI/CDIaCObservability

$ ls -la ./projects/

Projects

GitHub

Real-Time LLM Inference Pipeline

↑ 70% accuracy improvement

Designed and shipped a real-time LLM inference backend using FastAPI + Redis Streams + WebSockets for an automated fence-detection system. Replaced a manual verification workflow and reduced processing latency significantly.

FastAPIRedisWebSocketsPythonLLMAWS

Hospital Nurse Allocation System

↑ 35% better resource utilization

Built a real-time resource allocation platform for 15 hospitals serving 5,000+ daily patients. Event-driven architecture with WebSocket push, Redis pub/sub, and a rule-based scheduling engine — zero manual dispatch errors in production.

Node.jsWebSocketsRedisPostgreSQLReactTypeScript

AI-Powered Elderly Care Platform

↑ 30% caregiver efficiency increase

End-to-end platform serving 45 care facilities and 1,000+ daily residents. Real-time caregiver alerts, incident logging, and AI-driven care scheduling via WebSockets + Redis. Used by nursing staff in live clinical environments.

Next.jsNode.jsWebSocketsRedisPostgreSQLAWS

↑ 500+ orders/month, +10% revenue

E-Commerce Platform (Stripe + Next.js)

Full-stack multi-tenant e-commerce platform handling 500+ monthly orders. Features real-time inventory, Stripe payment flows, automated order tracking, and push notifications. Boosted client revenue by 10% post-launch.

Next.jsNode.jsStripe+3

↑ 80% faster delivery cycles

Telemedicine Mobile App

Secure React Native telemedicine app with WebRTC video consultations, appointment management, and real-time scheduling. Integrated automated GitLab CI/CD pipelines, reducing delivery cycle time by 80%.

React NativeNode.jsWebRTC+3

↑ Deployment complexity cut by 40%

Cloud Infrastructure Standardization

Standardized AWS infrastructure for a 20-engineer team using Pulumi IaC. Consolidated multi-service deployments across ECS, Lambda, API Gateway, and CloudFront. Built observability dashboards and automated CI/CD across all services.

AWSPulumiDocker+3

$ cat ./blog/*.md | head

Writing

All posts

Safe & Smart Use of AI in Development: What I Tell Every Junior Engineer

AI tools are the most powerful thing to hit software development in a decade. They're also the easiest way to build a career on a foundation of sand. Here's what I cover when I talk to a group of junior developers — honest, practical, and no hype.

Apr 15, 2026

11 min read

AICareer

Real-Time at Scale: Building a Hospital Nurse Allocation System for 5,000 Patients/Day

When a client needed to coordinate nurse assignments across 15 hospitals in real time, polling wasn't going to cut it. Here's the architecture we landed on — WebSockets, Redis pub/sub, and a rule-based scheduling engine that runs without a hitch in a live clinical environment.

Feb 18, 2026

9 min read

Node.jsWebSockets

LLM Inference Pipelines in Production: FastAPI, Redis Streams, and WebSocket Streaming

Shipping an LLM-powered feature to production is different from running a notebook. Here's how we built a real-time inference pipeline for an automated detection system — handling job queuing, streaming responses, and failure recovery at the backend level.

$ cat contact.json

Get In Touch

I'm actively looking for Senior Engineer or Tech Lead roles. If you're hiring or know of a good fit, I'd love to hear from you.

Also open to freelance, contract, and technical advisory work. I reply within 24 hours.

Send me an emailopens your mail app

me@hassams.dev

linkedin.com/in/hassams

github.com/hassams

@hassams