Hello, I am Ziraddin

I build production-oriented machine learning systems that transform research ideas into scalable, reliable applications. My work focuses on designing end-to-end ML platforms, including modular Retrieval-Augmented Generation (RAG) pipelines, computer vision models, and LLM-based applications with clear architectural separation across ingestion, feature processing, retrieval, inference, evaluation, and monitoring components.

Across projects, I develop reproducible ML workflows supported by experiment tracking with MLflow, containerized model services using FastAPI and Docker, and scalable deployments orchestrated with Kubernetes. CI/CD pipelines automate testing, packaging, and model delivery, while monitoring stacks such as Prometheus, Grafana, and Evidently AI provide visibility into system behavior and model performance in production environments.

Tech Stack

Core tools and technologies I use regularly

Python Python
NumPy NumPy
Pandas Pandas
Scikit-learn Scikit-learn
PyTorch PyTorch
TensorFlow TensorFlow
Keras Keras
FastAPI FastAPI
Docker Docker
Kubernetes Kubernetes
Airflow Airflow
GitHub Actions GitHub Actions
Prometheus Prometheus
Grafana Grafana
Apache Spark Apache Spark
AWS AWS
GCP GCP
Azure Azure
Linux Linux
Git Git

Latest Projects

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Cloud-Native ML Deployment Architecture (Docker, Registry, GKE)

Cloud-Native ML Deployment Architecture (Docker, Registry, GKE)

Building and deploying a machine learning model to predict diabetes risk using patient health data, packaging the model in a Docker container, exposing it through a FastAPI service, and deploying it to Kubernetes with an automated CI/CD pipeline.

container registry kubernetes CI/CD
Open Code Demo
Engineering a Production-Grade LLM Tutor for Structured Mathematics Reasoning

Engineering a Production-Grade LLM Tutor for Structured Mathematics Reasoning

Engineering a production-grade LLM-powered IGCSE mathematics tutoring platform by integrating a React frontend, a Django backend, and PostgreSQL persistence, orchestrating controlled model inference with structured prompt governance, containerizing the system with Docker, and implementing monitoring

LLM Architecture Prompt Engineering MLOPs
Open Code
Operationalizing Tabular ML: CI/CD, Docker, Kubernetes, Observability

Operationalizing Tabular ML: CI/CD, Docker, Kubernetes, Observability

Building a regression pipeline to predict housing prices using the California housing dataset, applying preprocessing and feature engineering with scikit-learn, tracking experiments with MLflow, serving predictions through a FastAPI API, containerizing the service with Docker, monitoring it using Pr

EDA GitHub Actions MLFlow
Open Code
Building a RAG Question Answering System Using LLM models and Vector Databases

Building a RAG Question Answering System Using LLM models and Vector Databases

Developing a retrieval-augmented question answering system over 7,500+ pages of Cambridge IGCSE Mathematics past papers by ingesting and chunking exam PDFs, generating embeddings and indexing them in a vector database, retrieving relevant context through semantic search, and generating step-by-step

RAG LangChain ChromaDB
Open
Web Scraping and Analysis of Job Market Data in Germany

Web Scraping and Analysis of Job Market Data in Germany

Collecting and structuring 22K+ job listings from a German job portal using a Python web scraping pipeline, enabling large-scale analysis of sector demand, job distribution, and geographic employment patterns across Germany.

data exploration web scraping
Open Code
Applied Data Science: Analytics, Visualization, and Machine Learning

Applied Data Science: Analytics, Visualization, and Machine Learning

Projects exploring data analysis, machine learning, and interactive visualization using R. The work focuses on analyzing film industry trends, streaming platform datasets, and healthcare data to uncover patterns and meaningful insights.

Data Science & Analytics Projects R
Open Code