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MuhammedZohaib/README.md

Muhammad Zohaib

Full‑Stack • DevOps • Applied AI/ML (3–5 yrs experience)

Typing SVG

I build end‑to‑end products: fast APIs, data/ML pipelines, and containerized deployments.
Focus: clean architecture, observability, automation, and model-to-production.

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Operating Principles

  • Ship > polish forever: deliver value in iterations, with tight feedback loops.
  • Production mindset: versioning, testing, monitoring, rollback paths.
  • Data/ML with engineering rigor: reproducible pipelines, measurable metrics, clear baselines.

What I do (end-to-end)

  • Full‑Stack Engineering: build APIs + web apps with authentication, RBAC, and clean domain boundaries.
  • DevOps & Platform: containerization, CI/CD pipelines, environment parity, infra automation.
  • Applied AI/ML: classical ML + deep learning + NLP, with deployment patterns (batch/online inference).
  • MLOps basics: experiment tracking, dataset/version hygiene, model packaging and rollout.

Core Focus Areas

Domain What I deliver
Backend FastAPI services, REST, async workers, background jobs, caching
Data preprocessing, feature engineering, pipelines, evaluation & metrics
AI/ML NLP, transformers, classification, retrieval/search, model serving
DevOps Docker, CI/CD, environment management, reliability practices
Databases schema design, indexing, query tuning, data access patterns

Tech Stack (toolbox)

Languages

Python JavaScript TypeScript

Backend • APIs

FastAPI Node.js

AI/ML • Data

PyTorch TensorFlow Scikit-Learn NumPy Pandas Jupyter

DevOps • Deployment

Docker Linux GitHub Actions

Databases

MySQL PostgreSQL

Highlighted Work

Pinned repos tell the full story, but here’s the kind of work I typically ship:

  • AI-powered APIs: model-backed endpoints (classification, similarity, retrieval) with versioned inference.
  • Data pipelines: repeatable preprocessing + evaluation + training runs (baseline → iteration).
  • Production deployments: Dockerized services with CI/CD, environment configs, and health checks.
  • Performance & reliability: caching, async tasks, DB indexing, structured logs.

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Production Checklist (how I build)

  • Testing: unit tests for core logic; integration tests for API paths
  • Quality gates: lint + format + tests in CI
  • Observability: structured logs, metrics mindset, clear error handling
  • Release hygiene: semantic versioning, changelogs, rollback strategy
  • Security basics: secrets management patterns, least-privilege mindset

Contribution Graph

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Contact


If you’re building a product that needs solid engineering + applied ML, let’s talk.

Pinned Loading

  1. HitFit-Gym-App HitFit-Gym-App Public

    A Java and JavaFX-based Gym Management App that enables user login/signup and provides administrative features for efficient gym management. Simplify and streamline your gym operations with this us…

    Java 9 4

  2. Fleet-Project Fleet-Project Public

    This repository houses a Java and JavaFX-based flight reservation project. It offers a straightforward interface for users to book seats on flights. The project utilizes file handling to store and …

    Java

  3. RDBMS-Simulator RDBMS-Simulator Public

    A Java and JavaFX application that connects to a MySQL database, enabling users to generate queries through the GUI and view the query results directly on the interface. Simplify database interacti…

    Java 1

  4. GlobalTechJobs GlobalTechJobs Public

    A central hub for tech job applications.

    Python 1

  5. Enable-HyperV Enable-HyperV Public

    A PowerShell script to enable or install Hyper-V along with Hyper-V Manager

    Batchfile