< Backend Engineer />

Muhammad Dawood Khan

Building high-throughput, containerized backend systems — architected for scale.

go run cmd/api/main.go --env=production

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About

Muhammad Dawood Khan is a Backend Software Engineer building and deploying production-grade distributed systems and scalable APIs. From database schema design to building containerized microservices and automated release pipelines, his focus is on system reliability, performance, and efficiency.

He has shipped a live property valuation backend service, designed database modeling scripts to support twin-cities IT labor economics research, and engineered containerized data pipelines deployed on Railway and AWS.

0.00

Dice Score (Medical Imaging)

Live on Railway

Property Valuation API

Production Projects Shipped

Active Collaborator

PhD AI Labor Market Thesis

ARSENAL

Core Competencies

System Architecture

REST APIs Event-Driven (EDA) Microservices Saga Pattern Circuit Breakers Row-Level Security

Backend Runtimes & Protocols

Python (AsyncIO) FastAPI Pydantic v2 gRPC / Protobuf GraphQL WebSockets

Databases & Cache

PostgreSQL Redis PgBouncer Alembic ClickHouse SQL Optimization

DevOps, Security & SRE

Docker & Compose Kubernetes GitHub Actions OAuth 2.0 / JWT Prometheus / Grafana OpenTelemetry

View Comprehensive Tech Stack

Shipped Work

Live on Railway

propval-pk

Pakistan Property Valuation API. End-to-end MLOps pipeline for real estate price prediction. Integrates Bayesian target encoding and quantile regression for uncertainty-aware predictions.

LightGBM MLflow DVC FastAPI Docker Railway

0.92 Dice Score

Brain Tumor Segmentation

Medical Imaging pipeline. Implements 2D MRI segmentation using a customized ResNet-34 semantic segmentation model with specialized data augmentations and mixed-precision training.

PyTorch ResNet-34 Segmentation

Deployed

textile-scheduler

Production Web App. A full-stack monorepo, end-to-end deployed, solving CORS, containerized caching and builds, and establishing a robust production release pipeline.

FastAPI React Vite Docker Railway

Competition Pipeline

SOFTEC 2025 ML Competition

Ensemble Pipeline for predictive ML. Engineered a six-model ensemble framework with automated Optuna hyperparameter sweeps and integrated TabNet/CatBoost estimators.

LightGBM XGBoost CatBoost TabNet PyTorch Optuna

Active

PhD Thesis Collaboration

AI Labor Economics. Led machine learning and statistical modeling (Random Forest & Logistic Regression) to analyze the impact of AI adoption on twin cities' IT labor market.

Scikit-learn Statsmodels Random Forest Logit Regression

Client Project

Image Classification Pipeline

Delivered a highly reproducible image classification pipeline fine-tuning ResNet models on STL-10, with automated evaluation reporting and structured hydra configuration management.

PyTorch ResNet Transfer Learning

Experience

PhD Thesis Collaboration (ML & Econometrics)

2025 – Present

Executing machine learning and econometric modeling (Random Forest, Logistic Regression, K-Means Clustering) on survey data from IT firms to analyze labor market dynamics, reskilling needs, and job displacement risks.

Freelance Backend Engineer

2024 – Present

Contracted by diverse clients to design and deploy scalable backend services and APIs. Built robust containerized data systems, engineered configuration management tools, and implemented automated logging, metrics, and health check monitoring.

Certifications

Verified

Let's Build Something

Open to Backend Engineering, DevOps, and Database Optimization opportunities. Based in Pakistan, available remotely. Let's build something scalable.

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Undergoing Upgrade