Senior ML Engineer: Build & Deploy Production Models

Datazza Group; Fatih; On-site; TRY 400,000 - 660,000; Full time

Job summary

Datazza Group in Istanbul is seeking an experienced Senior Machine Learning Engineer to join our team. You will design, develop, and deploy machine learning solutions in production environments, collaborating with data engineering to scale models across systems.

The ideal candidate has strong Python/SQL skills, deep knowledge of ML algorithms and model evaluation, and hands-on experience with PyTorch or TensorFlow, as well as MLflow, Airflow or Kubeflow for MLOps.

Qualifications

• Strong experience in Python and SQL.
• Solid knowledge of machine learning algorithms, statistics, and model evaluation.
• Hands-on experience with Scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow.
• Experience with MLOps tools such as MLflow, Airflow, or Kubeflow.
• Experience with Apache Spark and large-scale data processing.
• Familiarity with Docker, Kubernetes, and CI/CD practices.
• Experience deploying and maintaining ML models in production.
• Strong analytical and problem-solving skills.

Responsibilities

• Develop, train, and optimize machine learning models.
• Build and maintain end-to-end ML pipelines.
• Work on feature engineering, model evaluation, and performance optimization.
• Deploy and monitor ML models in production.
• Develop scalable ML solutions using large datasets.
• Collaborate with data engineering teams on ML architecture and integration.

Skills

Python

SQL

ML algorithms

Model evaluation

Scikit-learn

PyTorch

TensorFlow

Data analysis

Problem solving

Tools

MLflow

Airflow

Kubeflow

Apache Spark

Docker

Kubernetes

CI/CD

Job description

Datazza Group in Istanbul is seeking an experienced Senior Machine Learning Engineer to join our team. You will design, develop, and deploy machine learning solutions in production environments, collaborating with data engineering to scale models across systems.

The ideal candidate has strong Python/SQL skills, deep knowledge of ML algorithms and model evaluation, and hands-on experience with PyTorch or TensorFlow, as well as MLflow, Airflow or Kubeflow for MLOps.