Mahabub Ahmed

Mahabub Ahmed

Cloud & DevOps Engineer

Building cloud-native infrastructure with Kubernetes, AWS, Terraform, and CI/CD automation.

Professional Summary

Software Engineer with hands-on experience building and deploying containerized applications using Docker and Kubernetes. Skilled in designing CI/CD pipelines with GitHub Actions and Jenkins, and deploying infrastructure on AWS. Passionate about automation, infrastructure as code, and improving system reliability

Technical Skills

Cloud

AWS

Containers

Docker Kubernetes Helm EKS

Infrastructure as Code

Terraform Ansible

CI/CD

GitHub Actions Jenkins Argo CD

Programming Languages and Frameworks

Python JavaScript Go C C++ Node.js FastAPI REST APIs

Monitoring

Prometheus Grafana

Portfolio & Projects

Kubernetes-Based Full-Stack Deployment Platform

DevOps

Angular • FastAPI • PostgreSQL • Kubernetes • Helm • Docker • GitHub Actions

Tech Stack

Angular FastAPI PostgreSQL Kubernetes Helm Docker GitHub Actions Nginx

Problem

Needed a repeatable way to deploy a full-stack app with clean environment separation and low manifest duplication.

Deployment Flow

Built frontend and backend containers, pushed them through CI, and deployed to Kubernetes with Helm.

Lessons Learned

Helm improved reuse, and multi-stage builds kept the deployment lightweight and consistent.

View on GitHub

Secure Acquisitions REST API

DevOps

Node.js • PostgreSQL • Docker • GitHub Actions • JWT

Tech Stack

Node.js Express PostgreSQL Docker GitHub Actions JWT RBAC Rate Limiting

Problem

Needed a secure backend with authentication, access control, and a reliable automated build workflow.

Deployment Flow

Ran CI tests, built the Docker image, pushed it through the pipeline, and deployed the API with validation checks.

Lessons Learned

Security middleware should be part of the base design, and release pipelines should validate both code and containers.

View on GitHub

MERN Task App

DevOps

Full-Stack Task Management Platform

Tech Stack

Node.js Express React MongoDB Docker Jenkins AWS Docker Compose

Problem

Needed a containerized task app with a stable deployment flow across environments.

Deployment Flow

Built and linked frontend and backend containers with Docker Compose, then used Jenkins to automate testing and delivery.

Lessons Learned

Clear service boundaries make orchestration simpler, and automation improves deployment predictability.

View on GitHub

Go Ecommerce

DevOps

E-commerce Microservices Platform

Tech Stack

Go PostgreSQL Docker REST API JWT Microservices

Problem

Needed a production-ready backend with secure authentication and clear service boundaries.

Deployment Flow

Built the Go services, connected PostgreSQL, containerized the stack, and used Docker for repeatable deployment.

Lessons Learned

Smaller service boundaries improve maintainability, and containers make release behavior more predictable.

View on GitHub

AI Agent System

AI

Multi-Tool Orchestration Platform

Architecture Diagram

Architecture diagram for AI Agent System

Tech Stack

Python LangGraph Chainlit

Problem

Needed a controlled agent workflow with reliable tool routing and decision visibility.

Deployment Flow

Built the orchestration in Python, connected tools with LangGraph, and used Chainlit for debugging.

Lessons Learned

Deterministic routing improves reliability, and observability makes agent workflows easier to maintain.

View on GitHub

Voice Order App

AI

Speech-to-Order Intelligence Platform

Architecture Diagram

Architecture diagram for Voice Order App

Tech Stack

Python Whisper OpenAI API Gradio

Problem

Needed a voice workflow that could turn speech into structured orders while handling ambiguity.

Deployment Flow

Built speech-to-text, added entity extraction, and used follow-up prompts to resolve uncertain inputs.

Lessons Learned

Correction logic improves accuracy, and structured prompts reduce parsing errors downstream.

View on GitHub

Spotify Prediction

MLOps

End-to-End MLOps Pipeline

Architecture Diagram

Architecture diagram for Spotify Prediction

Tech Stack

Dagster MLflow XGBoost FastAPI Streamlit Docker

Problem

Needed an automated ML workflow for training, tracking, and serving predictions.

Deployment Flow

Orchestrated training and evaluation with Dagster, tracked runs with MLflow, and served predictions through FastAPI and Streamlit.

Lessons Learned

MLOps is more stable when orchestration, tracking, and serving are designed together.

View on GitHub

Experience

Software Engineering Intern

PixScrib (Gaming Platform Startup), Germany | Apr 2026 – Present

  • Developed backend features and resolved issues for an online gaming platform.
  • Implemented GitHub webhook integrations for automated development and deployment workflows.
  • Contributed to Docker-based deployment workflows and GitHub Actions pipelines to improve release consistency.
  • Supported monitoring, troubleshooting, and production support to improve service reliability.

Assistant Engineer

Vicar Electricals Ltd, Bangladesh | Mar 2018 – Jun 2021

Applied engineering principles to transformer design and assembly. Improved quality control processes, reducing defects and increasing manufacturing consistency.

Education

42 Heilbronn gGmbH

Heilbronn, Germany | Oct 2024 – Present

M.Sc. in IT Management

IU Internationale Hochschule, Germany | 2022 – 2023

B.Sc. in Electrical and Electronic Engineering

AUST, Bangladesh | Dec 2017

Certificates

Certificate 1

LEVEL3 - MLOps

2025

LEVEL3

Certificate 2

STACKIT Kubernetes Fundamentals

2026

STACKIT University