Available for Full-Stack / AI Engineering roles

Syed Muhammad Irtaza

I build scalable full-stack products, AI workflows & cloud systems that solve real business problems.

Full-Stack Software Engineer — AI Workflows · Cloud Systems · Scalable Product Engineering

I work end-to-end across frontend, backend, cloud infrastructure, automation, and AI-powered workflows — turning messy business problems into production systems that ship and scale.

View Case StudiesContact MeDownload Resume
  • Next.js
  • NestJS
  • PostgreSQL
  • AWS
  • Flowise
  • n8n
  • Stripe
  • AI / RAG
system.status — Irtaza LIVE
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syed_muhammad_irtaza
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STATUSAvailable for Full-Stack / AI Engineering roles
FOCUSProduct Engineering · AI Workflows · Cloud Systems
LOCATIONMontreal, Canada
CORENext.js · NestJS · PostgreSQL · AWS
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Featured Work

Engineering case studies

Real systems shipped to production — the problem, what I built, and the measurable impact. Not a wall of screenshots.

AI Property Report Generator preview
01Featured
AI / LLM Workflow

AI Property Report Generator

Problem

Property data was locked inside listing URLs and needed to become useful, personalized reports — a slow, manual analysis process.

Built

An LLM-powered workflow that scrapes a property URL, analyzes the listing details, evaluates location context, derives mortgage-related insights, and formats a final structured report delivered to the user.

ImpactManual → Auto

Automated a previously manual analysis and reporting process, creating a repeatable, scalable way to generate property insights on demand.

  • Built Flowise AI agents that generate contextual reports directly from a property URL.
  • Resilient scraping with fallbacks to keep listing data available and reliable.
  • Location, pricing and mortgage context fused into one personalized report.
  • Output formatted for delivery via report / PDF / email flow.
  • Flowise
  • Next.js
  • NestJS
  • LLM Agents
  • Web Scraping
  • PDF / Email
Visit habily.es
AI Product Feature02
User Session
auth token
Chat API
NestJS
n8n / Flowise
workflow
Persisted History
DB

Custom AI Chatbot & Chat Workflow

Problem

An embedded third-party chatbot widget was limited — it couldn't carry user-specific context or persist conversations across sessions.

Built

A custom chatbot UI integrated into the platform with authenticated user sessions, backend chat APIs, persisted chat sessions, and n8n / Flowise workflow automation behind the responses.

ImpactWidget → Feature

Turned a throwaway chat widget into a central, context-aware product feature backed by real data and workflows.

  • Next.js
  • NestJS
  • n8n
  • Flowise
  • Auth Tokens
  • PostgreSQL
Cloud / Cost Engineering03
Bucket Analysis
prod / stage / dev
Access Logs
Athena
Lifecycle + Tiering
auto cleanup
Lower Spend
$800 → <$400

AWS S3 Cost Optimization System

Problem

Media-heavy platforms were generating high S3 storage costs spread across production, staging and development buckets with no visibility.

Built

A cost-optimization strategy: bucket segmentation and analysis, lifecycle policies, access logging, automated cleanup rules, and intelligent storage tiering for infrequently accessed assets.

Impact~50%

Targeted a roughly 50% monthly S3 cost reduction — from around $800/month toward under $400/month — while keeping assets available.

  • AWS S3
  • CloudFront
  • Athena
  • Lambda
  • Lifecycle Rules
  • Intelligent-Tiering
Payments / Operations04
Credit Check
validation
Invoice Gen
metadata
Approval
review
Payout Job
Bull queue

Quick Payment & Invoice Automation

Problem

Creator and business payments were manual or delayed, creating operational friction and slow turnaround for finance teams.

Built

A quick-payment flow with business credit validation, user/payment metadata, invoice generation, an approval step, and background jobs to process payouts reliably.

Impact~80%

Helped cut quick-payment processing time dramatically and streamlined internal payment operations.

  • NestJS
  • Node.js
  • Sequelize
  • PostgreSQL
  • Stripe
  • Bull Queues
Influencer Campaign Platform preview
05Product Engineering

Influencer Campaign Platform

Problem

Brands needed better ways to search, filter, invite and manage creators across campaigns at scale.

Built

Creator search with complex filters, campaign management tooling, reporting features, invoice reminders, and targeted performance optimizations across a large creator dataset.

Impact10M+

Improved creator discovery, campaign management and operational visibility across the platform.

  • React
  • Next.js
  • Node.js
  • Sequelize
  • PostgreSQL
  • AWS
AI / LLM Engineering

AI systems, wired into real products

I build practical AI features — not chat demos. Each workflow moves from raw input to a structured, delivered result.

workflow.pipeline
  1. step 1
    Input
    URL / User question
  2. step 2
    Retrieve
    Scraping / context
  3. step 3
    Reason
    LLM agent / workflow
  4. step 4
    Structure
    Validated output
  5. step 5
    Deliver
    Report / chat / email

In the toolbox

The pieces I reach for when building AI features.

  • LLM agent workflows
  • Flowise apps
  • n8n automations
  • RAG-oriented retrieval
  • Property report generation
  • Chatbot integration
  • Scraping + AI analysis
  • LangSmith tracing
note.md

These are shipped features, not demos — practical AI plumbing wired into real products: authenticated sessions, persisted state, scraping fallbacks, structured outputs, and delivery flows.

in "property-url"
→ scrape → analyze → reason → format
out structured_report.pdf
Technical Stack

The stack, as a command explorer

Pick a category to browse the tools I build with day to day.

stack — explorer

Frontend

Fast, accessible product UIs

Reactcore
Next.jsApp Router
TypeScript
Tailwind CSS
MUI
Redux / RTK Query
Experience

A track record of shipping

Three roles, one throughline: owning features end-to-end and putting them in production.

  1. Aug 2025 — PresentCurrent

    Freelance Developer

    Independent · Remote

    Partnering with teams and founders to build full-stack products, AI workflows, and cloud systems as an independent engineer.

    • Design and ship full-stack features end-to-end — database to UI.
    • Build AI / LLM workflows and automations (Flowise, n8n) into real products.
    • Set up and optimize cloud infrastructure and deployments on AWS.
    • Deliver dashboards, payment flows, and scalable backend APIs.
    • Next.js
    • NestJS
    • PostgreSQL
    • AWS
    • Flowise
    • n8n
  2. May 2024 — Aug 2025

    Full-Stack Developer

    Ideamappers / Numu

    Full-stack product engineering across an influencer-marketing platform and AI-driven property tooling.

    • Shipped full-stack product features end-to-end across web and services.
    • Built AI / LLM integrations — report generation and a custom chatbot feature.
    • Led AWS cost optimization targeting ~50% lower S3 spend.
    • Delivered payment / invoice flows and creator search dashboards.
    • Added white-label theming and backend performance improvements.
    • Next.js
    • NestJS
    • PostgreSQL
    • AWS
    • Flowise
    • n8n
    • Stripe
  3. Sep 2022 — Mar 2024

    MERN / Full-Stack Developer

    Codistan Ventures / Paklogics

    Built and shipped e-commerce and marketplace platforms across the MERN stack.

    • Developed e-commerce and marketplace platforms with React / Next.js.
    • Built Node.js backends and GraphQL / REST APIs.
    • Integrated Stripe payment flows and MongoDB data layers.
    • Improved performance and migrated legacy code toward TypeScript.
    • React
    • Next.js
    • Node.js
    • GraphQL
    • MongoDB
    • Stripe
Impact

Outcomes, not just output

A few numbers that stand behind the work. Figures are targets and estimates drawn from real projects.

~80%
Faster quick-payment processing

Helped cut payment turnaround with validation + queued payouts.

~50%
Targeted S3 cost reduction

Strategy aimed at $800/mo toward under $400/mo.

5×
API response improvement

Refactored endpoints: query time 6s → 1.2s.

10M+
Creator profiles searchable

Complex filters over a large creator dataset.

Built AI report + chatbot workflows from scraped property data.
Developed payment, invoice, creator-search and campaign systems.
Worked across frontend, backend, cloud and AI workflows.
Portrait of Syed Muhammad Irtaza
Syed Muhammad Irtaza
Full-Stack Software Engineer · Montreal, Canada
About

Engineer who ships business outcomes

I'm a full-stack software engineer based in Montreal, Canada, building production-ready web platforms, backend systems, AI workflows, and cloud-based infrastructure.

I like solving business problems through practical engineering. My work spans influencer marketing, property technology, e-commerce, payments, dashboards, automation, and AI-powered workflows — usually owning a feature from database to UI.

Right now I'm going deeper into AI engineering, RAG systems, and scalable cloud architecture — connecting LLM workflows to real product surfaces instead of one-off demos.

Based in
Montreal, Canada
Focus
Full-stack · AI · Cloud
Currently
Deepening AI / RAG + cloud architecture
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Let's build something solid

Have a product, AI workflow, or engineering problem worth building? Let's connect.

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