Muhammad Mansoor · SaaS & AI Product Engineer

I Help Founders Build Scalable SaaS & AI Products

Product engineering for startups — from technical architecture and backend systems to complete web, mobile and AI product delivery.

3+ years building systems across SaaS, fintech, enterprise workflows and applied AI.

Services

What I Help Build

I work with founders and product teams on the technical path from idea to production — choosing the right architecture, building core systems and bringing in specialist expertise where the product requires it.

SaaS Products

Multi-tenant platforms, authentication, payments, dashboards and scalable product architecture.

  • Multi-tenancy
  • RBAC
  • Payments
  • Product APIs

AI Products

RAG, semantic search, AI assistants and intelligent features built around real business workflows.

  • RAG
  • Vector Search
  • AI Integrations
  • Intelligent Workflows

Web & Mobile Products

End-to-end product delivery with the right technical stack and specialist collaborators.

  • Web Platforms
  • Mobile Products
  • API Integration
  • Product Delivery

Existing Product Development

New features, integrations, architecture improvements and engineering problem solving.

  • Feature Development
  • Integrations
  • Architecture
  • Production Problems

Selected Work

Products and Systems I've Helped Build

Across SaaS, AI and distributed platforms — product thinking first, technology second.

SaaS Product Engineering

Multi-Tenant Event SaaS

Contributed to an end-to-end multi-tenant event platform involving tenant-specific experiences, role-based workflows, event management, payments and production infrastructure.

  • Multi-tenancy
  • RBAC
  • Event workflows
  • Payment integration
  • Production infrastructure
  • NestJS
  • Next.js
  • PostgreSQL
  • Stripe
  • Docker
  • GCP
View Case Study

Applied AI / RAG

AI Expert Discovery Platform

Built a RAG-based expert discovery platform combining vector retrieval and LLM reasoning to move beyond keyword-only matching.

  • Semantic retrieval
  • Vector search
  • RAG
  • LLM reasoning
  • Expert discovery
  • Qdrant
  • Spring AI
  • FastAPI
  • OpenAI
  • Gemini
  • Vector Search
View Case Study

Distributed Systems

Event-Driven SaaS Architecture

Worked on migrating a monolithic system toward multi-tenant microservices and asynchronous workflows.

  • Spring Boot microservices
  • Kafka
  • Tenant isolation
  • Asynchronous processing
  • Independent deployments
  • Java
  • Spring Boot
  • Kafka
  • Docker
View Case Study

How I Work

From Product Idea to Production

Good software starts with understanding the product before choosing the technology.

  1. 01

    Understand the Product

    Clarify the users, business problem and the product outcome.

  2. 02

    Define the MVP or Problem

    Separate the essential product workflow from features that can wait.

  3. 03

    Design the Technical Path

    Choose architecture, integrations and technology based on actual product requirements.

  4. 04

    Build With the Right Specialists

    Lead the technical delivery and involve trusted frontend, mobile or design expertise when needed.

  5. 05

    Test and Deploy

    Validate critical workflows, integrations and production readiness.

  6. 06

    Improve From Real Feedback

    Use product usage and real engineering signals to guide the next iteration.

Technical Authority

Deep Technical Foundations

My product engineering approach is grounded in backend architecture, distributed systems and applied AI.

Backend & Distributed Systems

Core
  • Java
  • Spring Boot
  • Spring Security
  • Spring Cloud
  • NestJS
  • FastAPI
  • REST APIs

Event-Driven Systems

Core
  • Kafka
  • RabbitMQ
  • Microservices
  • Distributed Systems

Applied AI

Core
  • RAG
  • Vector Search
  • Qdrant
  • OpenAI
  • Gemini
  • Spring AI
  • LLM Integration

Data & Infrastructure

  • PostgreSQL
  • Redis
  • Qdrant
  • Docker
  • GCP
  • CI/CD

Product Delivery

  • Next.js
  • React
  • TypeScript
  • Stripe
  • WebSockets
  • Cloud Integrations

About

About Mansoor

Muhammad Mansoor, SaaS and AI product engineer

Broad delivery.
Deep technical authority.

I'm a software engineer with 3+ years of professional experience building systems across fintech, SaaS, enterprise workflows and AI.

I started deep in Java and Spring Boot, moved through microservices and distributed systems, and more recently expanded into applied AI and RAG products. My core strength is backend architecture — but the more complete products I worked on, the more I learned to think beyond a single layer of the stack.

A user doesn't see a backend problem, a frontend problem or an infrastructure problem. They see one product.

That's why I now approach software as a product engineer — understanding the business workflow, designing the technical path and helping move the product toward production. Today I'm building my independent product engineering practice and documenting what I learn along the way.

Based in Lahore, Pakistan. Working with products and teams internationally.

Background

Engineering Experience

Software Engineer

Adverta

Nov 2025 - Jun 2026
  • Production RAG
  • Qdrant
  • Spring AI
  • Multi-tenant SaaS
  • Stripe
  • NestJS
  • GCP · Docker · CI/CD

Java Developer

Kale Labs

Sep 2024 - Oct 2025
  • Spring Boot
  • Multi-tenant microservices
  • Kafka
  • Event-driven architecture
  • Production stabilization
  • Salesforce modules

Associate Software Engineer

InfoTech Group

May 2023 - Sep 2024
  • Core banking (T24 · Misys)
  • Spring Boot APIs
  • Camunda BPMN
  • Enterprise automation
  • Node.js chatbots

Full experience, education & certifications

Contact

Building Something?

Whether you're validating a SaaS idea, adding AI to an existing product, or solving a complex engineering problem — let's talk about the product and the technical path behind it.

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