system online — open to opportunities

I build intelligent systems from models to interfaces — trained, tracked, shipped.

12 public repos AI + MLOps focus Islamabad, PK

EXPERIMENT LOG

Selected runs

06 experiments · all merged to main.
Each run carries its own heat signature — hotter means fresher on the ramp.

RUN 001 GEOSPATIAL

Islamabad Land Price HeatMap

Automated land-price heatmap of Islamabad — spatial interpolation, choropleth layers, and a data-storytelling pass that makes the market legible.

PythonGeoPandasMaps
Open source
RUN 002 MLOPS

Tox21 Molecular Toxicity Platform

End-to-end MLOps platform predicting molecular toxicity — experiment tracking, model registry, and containerized deployment.

PyTorchMLflowDocker
Open source
RUN 003 ORCHESTRATION

Automated MLOps Pipeline

Airflow-orchestrated training pipeline with MLflow experiment tracking — repeatable, scheduled, observable ML delivery.

AirflowMLflowAutomation
Open source
RUN 004 LLM RESEARCH

DeepSpeed vs LoRA Adapters

Comparative fine-tuning study — full training under DeepSpeed against LoRA adapters, with cost, speed, and quality trade-offs measured.

DeepSpeedLoRALLM
Open source
RUN 005 BROWSER TOOL

Smart Right Click Extension

Productivity browser extension with AI-assisted right-click workflows — quicker actions, cleaner UX.

JavaScriptChrome APIAI
Open source
RUN 006 FULL STACK

Auth App

Production-style authentication system — secure user flows, session handling, and backend integration.

PythonAuthBackend
Open source

CAPABILITY MATRIX

Where I operate

A

AI / ML

RAG, classification, recommendation systems, vision experiments, and LLM workflows.

PyTorchTransformersOpenCV
B

Data Products

Trackers, heatmaps, explorers, scraping systems, and analytics-first interfaces.

PandasPlotlyStreamlit
C

MLOps / Delivery

Airflow, MLflow, Docker, CI/CD, and reproducible workflows for real deployment.

FastAPIDockerGitHub Actions
D

Web Tools

Browser extensions, auth flows, and frontend layouts that make work feel finished.

JavaScriptTypeScriptReact

TRAINING NOTES

Most models die in notebooks. I build the pipeline that gets them out — tracked in MLflow, shipped in Docker, orchestrated in Airflow, and wrapped in interfaces people actually want to use.

0 public repos
0 shipped experiments
0 technical lanes

FINAL EPOCH

LET'S SHIP SOMETHING REAL

GET IN TOUCH