Open to AI/ML & Back-End Engineering roles

Passionate about creating value through technology

A Computer Science graduate specializing in AI/ML & Back-End Engineering

How I live
BUILDDEPLOYReactFastAPINode.jsKafkaTemporalML inferencePostgreSQLGitDockerKubernetesAWS
01About

Engineer at the model–system boundary

I design and build scalable server-side systems, integrate AI/ML models into production applications, and ship full-stack solutions end-to-end. Right now I'm working on an enterprise platform for orchestrating autonomous AI agents.

I care about the unglamorous parts that make AI useful in production: clean APIs, durable workflows, observability, and systems that stay reliable under load. My favorite work sits exactly where machine learning meets real backend engineering.

Back-End

  • Python
  • Django
  • FastAPI
  • Flask
  • Node.js
  • Express.js

Front-End

  • JavaScript
  • React.js
  • HTML
  • CSS

Databases

  • PostgreSQL
  • MySQL
  • MongoDB
  • ClickHouse

ML & Infra

  • Machine Learning
  • Docker
  • Kubernetes
  • AWS EC2
  • Temporal
  • Kafka
  • Git
02Experience

My career, made interactive

Instead of telling you what I've worked on, I tried to let you experience it. Each role has a small interactive demo. Try it!

Devsinc

Associate Software Engineer

Jan 2026 — Present

Lahore, Pakistan · On-site

Wand AI — Enterprise AI Workforce Platform

Building high-impact backend capabilities for an enterprise AI platform, improving scalability, reliability, and customer experience through production-ready autonomous agent workflows.

  • Increased file upload limits from 100 MB to 1 GB, enabling richer context for AI agents and strengthening customer trust.
  • Built backend services and APIs powering scalable agent orchestration.
  • Developed durable distributed workflows for reliable long-running agent execution.
  • Created a self-improving agent pipeline that learns from conversation history and customer satisfaction signals overtime.

Self-improving agent

Support Agent

scripted demo

Agent v1 · base

Pick a question below to talk to the agent.

Self-improvement loop

Current tools

Knowledge Base

The base agent is limited. Ask a question, then run a self-improvement cycle — it learns from conversations + CSAT and upgrades itself.

Innovarix Systems

Software Engineer

Aug 2024 — Oct 2025

Remote

Web & Mobile Product Development

Delivered end-to-end web and mobile solutions for startup clients, helping businesses establish their digital presence, automate operations, and accelerate product delivery.

  • Built high-performing websites and digital brand experiences for client businesses.
  • Automated sales workflows to improve lead conversion and operational efficiency.
  • Shipped MVPs and prototypes rapidly, enabling faster product validation.
  • Owned features end-to-end across frontend and backend, from design to deployment.

Web & mobile apps

proboxpackages.com

Overview

Orders

812+11%

Revenue

$41k+9%

Quotes

156+18%

Revenue trend

30d

Pro-Box Packages

A packaging brand's full web presence — marketing site, CMS dashboard, and a mini storefront.

AALNO AI
Hi! I'm ALNO AI — ask me anything.

ALNO AI

All-in-one AI app: custom chatbots, image generation, and PDF chat — powered by GPT-4o & Claude.

CodSoft

Machine Learning Intern

Apr 2024

Remote

ML Fundamentals on Real Datasets

Built a strong foundation in machine learning by working with real-world datasets, mastering Python's ML ecosystem, and applying core concepts through hands-on experimentation in Jupyter notebooks.

  • Analyzed and prepared real-world datasets using Pandas and NumPy.
  • Built, trained, and evaluated machine learning models with scikit-learn.
  • Visualized data and model performance using Matplotlib.
  • Developed the practical ML fundamentals that laid the foundation for my AI engineering career.

Train a model

click anywhere to add a data point

6

points

0

epoch

4.16

loss

A linear model learns a line that minimizes the squared distance (the red residuals) to your data. Hitting Fit runs that optimization — watch the loss fall as the line settles into place.

Villaex Technologies

Python Developer Intern

Oct 2023 — Dec 2023

Lahore, Pakistan · On-site

Backend Web Development with FastAPI

Laid the foundation for my software engineering career by building backend services with Python and FastAPI while learning collaborative development practices and the software development lifecycle in a production environment.

  • Built REST APIs and backend services using Python and FastAPI.
  • Applied Git and GitHub workflows in a collaborative engineering team.
  • Learned software development best practices across the SDLC.
  • Established the engineering fundamentals that shaped my backend development career.

API playground

Endpoints
GET/api/health

Response

Hit Execute to send the request.

03Education & Research

Neural compression, explained simply

Traditional codecs compress every image using the same fixed set of mathematical rules. Neural compression takes a different approach by learning which information is most important to preserve rather then applying fixed compression, enabling higher visual quality at lower bitrates.

Bachelor of Computer Science

GIFT University

CGPA 3.72 / 4.0
Neural compressedNeural180 KB
JPEG compressed
JPEG180 KB

Drag to compare. At an identical 180 KB, the neural codec keeps detail that JPEG smears into blocks.

Final Year Project

Neural Image & Video Compression

My final-year project: a deep-learning compression system that outperformed traditional codecs in efficiency while preserving high visual quality, shipped as a full-stack web app

FastAPIReact.jsFirebasePyTorch

Learns what matters

Trained on real images to keep the details your eye actually notices — and discard the rest.

More quality per byte

Smaller files at the same perceived quality compared to JPEG or H.264.

Content-adaptive

Adapts to each image instead of applying the same fixed math everywhere.

Future-proof

Gets better as the models get better — no brand-new file format required.

How it works

Instead of storing every pixel, the network learns a compact representation of the image. During optimization, it gradually adjusts its parameters until it can accurately recreate that specific image using as little information as possible. The learned representation is then used to reconstruct the image whenever it is decoded.

inputencodelatentdecodeoutput

Controlling quality vs size

The final result depends on several factors—including the size of the latent representation, the network architecture, and the balance between compression and reconstruction quality. Performance is commonly measured using PSNR, which indicates how closely the reconstructed image matches the original. The clever part: at any file size, this network keeps more quality than older formats like JPEG, because it learned which details actually matter to your eyes.

smaller file
lower PSNR
larger file
higher PSNR
Contact

Let's build something reliable

Open to AI/ML & Back-End Engineering roles. Whether it's AI agents, backend systems, or a full-stack build, I'd love to hear what you're working on.