Sarmad Ali
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Sarmad AliAvailable for work

Portfolio — 2026

Full-stack products,AI-powered tools,Production software.

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Sarmad Ali — Junior Software Engineer, Lahore, Pakistan

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About

Experience

AI Engineer Intern
CodeNinja

Jun 2026 — Sep 2026

Lahore, Pakistan

  • 01I worked across the front end and back end of the Performance Management System, an internal platform for tracking employee performance.
  • 02I extended the PMS MCP server with more precise tools and an ontology layer, and improved the platform's workflows.
  • 03I authored the implementation plan for multi-manager weighted ratings and an Autopilot agent pipeline, then handed both over to the team at the end of the internship.
  • 04I tested internal tools, diagnosed a default credential mismatch on a no-code platform, wrote a formal bug report, and resolved merge conflicts on the shared PMS repository.

Full Stack Engineer (Part-time)
Interstate Rankers

Apr 2025 — Dec 2025

Lahore, Pakistan · On-site

  • 01I built and maintained the company's main website and delivered production-ready websites for US-based clients.
  • 02I worked across the full stack with React, Next.js, TypeScript and the MERN stack, taking projects from requirements through deployment.

Software Development Intern (Mentorship)
E-Pay Punjab

Jan 2025 · 1 month

Punjab, Pakistan

  • 01I worked with the development team on E-Pay Punjab, a Government of Punjab payments platform, in a mentorship-style internship.
  • 02I learned core Java on the job, with senior developers reviewing what I wrote.

Customer Support Representative
Abacus

2024

Pakistan

  • 01I handled complex technical queries from customers, which helped both satisfaction and retention.
  • 02I tidied up the communication channels so responses went out faster.

01 / 03

BrowserPilot

AI Assistant & Browser Automation Platform

2026

  • TypeScript
  • Node.js
  • React
  • Chrome Extensions (MV3)
  • WebSockets
  • Chrome DevTools Protocol
  • MCP
  • OCR

I built this as a Manifest V3 Chrome extension with a WebSocket gateway behind it. The agent runs on your own machine and drives the session you're already signed into, so it never needs your passwords and never has to scrape from the outside.

Lead generation is the main thing it does. Find candidates, enrich them with emails, export the result. I made the pipeline resumable with caching, deduplication and atomic checkpoints, so a run that dies at step four picks up again at step four.

It also handles receipt OCR and expense reconciliation. Credentials sit in AES-256-GCM encrypted storage. There are 204 tests covering it.

204

automated tests

AES-256

GCM credential vault

MV3

Chrome extension

Architecture

  1. 01

    Extension

    Manifest V3

  2. 02

    WS gateway

    authenticated bridge

  3. 03

    Discovery

    seed → candidate set

  4. 04

    Enrichment

    email resolution

  5. 05

    Export

    atomic checkpoints

02 / 03

Sovereign AI Workspace

Model-Agnostic LLM Runtime

2026

  • TypeScript
  • Node.js
  • LLM APIs
  • Ollama
  • Prompt Engineering
  • AI Governance

Most LLM code ends up tied to whichever provider you started with. I wanted the model to be a part you can swap out, so this runtime moves between OpenAI Codex in the cloud and Ollama or Qwen locally without the calling code changing at all.

The work was in the parts every provider does slightly differently: structured and parallel tool calls, token budgeting, context window limits. Those sit behind one interface now, and conformance checks verify each backend actually does what it claims instead of taking its word for it.

It redacts logs so they're safe to audit, and ships GPU and memory sizing utilities so you can tell whether a model fits your hardware before downloading it. 281 tests pass against it.

281

passing tests

2

provider classes

0

code changes to swap models

Architecture

  1. 01

    Runtime core

    provider-agnostic

  2. 02

    Cloud adapter

    OpenAI Codex

  3. 03

    Local adapter

    Ollama / Qwen

  4. 04

    Budgeter

    tokens + context window

  5. 05

    Redactor

    audit-safe logs

03 / 03

WalkGen

AI Property Video Walkthrough Generator

2026

  • Node.js
  • MCP SDK
  • Replicate API
  • ffmpeg
  • Zod

WalkGen is a backend MCP server built around seven tools and a fire-and-poll flow. The client starts a job and polls for the result, rather than holding a connection open for the several minutes a render takes.

Image to video runs through Kling on Replicate. A custom ffmpeg pipeline then stitches the clips together and handles transitions and frame-accurate timing. A room-aware shot grammar decides how to move through each space, because a kitchen and a hallway don't want the same camera move.

Storage is just the filesystem with a self-healing layer on top, no database anywhere. Less to run, and you can check on a job by opening a folder.

7

MCP tools

0

database dependencies

fire & poll

execution model

Architecture

  1. 01

    Ingest

    listing photos

  2. 02

    Shot grammar

    room-aware

  3. 03

    Kling / Replicate

    image → video

  4. 04

    ffmpeg

    stitch + timing

  5. 05

    FS store

    self-healing

Practice

Languages

  • JavaScript (ES6+)
  • TypeScript
  • Java
  • HTML
  • CSS

Frontend

  • React
  • Next.js
  • React Native
  • Responsive UI/UX
  • Three.js
  • GSAP

Backend & Data

  • Node.js
  • Express.js
  • REST APIs
  • MongoDB
  • Authentication
  • WebSockets
  • Third-Party Integrations

AI & Engineering

  • Claude Code
  • Claude
  • ChatGPT
  • LLM APIs
  • Model Context Protocol
  • AI Agents
  • Automated Testing
  • Git & GitHub
  • Vercel
  • Production Debugging

Certifications

Anthropic

  • Claude with the API
  • Claude Code in Action
  • Intro to Agent Skills
  • Intro to MCP

Education

BS, Software Engineering
University of Central Punjab (UCP)

Lahore, Pakistan

Contact

asarmad314@gmail.com

Next.js, WebGL, GSAP.
Instrument Serif & Geist Mono.