Senior ML Engineer — Production AdTech Platform (Hybrid)

Wpp; Poland; Hybrid; PLN 180,000 - 260,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

WPP is seeking a Machine Learning Engineer to design, build and maintain scalable data and ML infrastructure for production AI models in the cloud. You will work with a team of data scientists and engineers to productionize research, implement CI/CD and data governance, and apply best practices for reliability and performance.

We operate in a hybrid setup with offices around four days a week. Strong Python/SQL skills, cloud experience (GCP/AWS/Azure) and familiarity with Spark/Flink, Docker, and

Qualifications

• Strong background in data, ML engineering or large-scale software engineering.
• Bachelor degree in CS/Engineering or related field is required.

Responsibilities

• Design, implement and maintain scalable, production-grade data and ML infrastructure, supporting our AI models in GCP.
• Collaborate with a team of 14+ data scientists and engineers to productionize research prototypes into robust systems.
• Contribute to data governance and quality frameworks to ensure reliable inputs.
• Identify and address technical debt to improve stability.
• Apply CI/CD, version control and testing best practices.

Skills

Python

SQL

GCP

AWS

Azure

Docker

Terraform

Spark

Flink

Airflow

Dagster

PyTorch

TensorFlow

MLflow

Kubeflow

CI/CD

Version control

Testing

AdTech data

Education

Bachelor's degree in Computer Science or Engineering

Tools

GCP

AWS

Azure

Job description

WPP is seeking a Machine Learning Engineer to design, build and maintain scalable data and ML infrastructure for production AI models in the cloud. You will work with a team of data scientists and engineers to productionize research, implement CI/CD and data governance, and apply best practices for reliability and performance.

We operate in a hybrid setup with offices around four days a week. Strong Python/SQL skills, cloud experience (GCP/AWS/Azure) and familiarity with Spark/Flink, Docker, and