Senior MLOps/Data Engineer

TomTom; Amsterdam; Presencial; EUR 90.000 - 120.000; Jornada completa

Descripción de la vacante

TomTom is looking for an experienced ML/MLOps engineer to own the end-to-end ML lifecycle on Azure and Databricks, collaborating with applied scientists to operate reliable models.

You will orchestrate pipelines from ingest to deploy, standardize experiments with MLflow, automate CI/CD, monitor data and models, ensure security, and handle incidents and post-mortems.

Formación

• Excellence in Python software engineering and developing tests.
• Fundamental understanding of Machine Learning.
• 3+ years in Data Eng/MLOps roles.
• Strong PySpark
• Hands-on with Databricks and Delta Lake
• CI/CD for data/ML (Git, PR workflow, automated tests, environment pinning)
• Azure basics
• Monitoring and building dashboards
• Clear communication; operational-excellence mindset (SLA/SLO ownership)

Responsabilidades

• Orchestrate and maintain ML pipelines (ingest → feature engineering → train → evaluate → deploy→monitor→repeat) on Azure + Databricks
• Standardize experimentation using MLflow or similar tools (tracking, artifacts, model registry, stages)
• Automate jobs with Databricks Workflows and CI/CD (GitHub Actions or Azure DevOps)
• Implement data & model observability: freshness/completeness, drift (features/model), training/serving skew, SLA/SLO monitoring
• Ensure security & compliance
• Handle incidents and post-mortems for ML pipelines and serving infrastructure

Conocimientos

Python

PySpark

Databricks

Delta Lake

Azure

CI/CD

Monitoring

Communication

Herramientas

MLflow

GitHub Actions

Azure DevOps

Descripción del empleo

What you\'ll do

In a nutshell, own the end-to-end ML lifecycle on Azure and Databricks, working with applied scientists to operate reliable models.

• Orchestrate and maintain ML pipelines (ingest → feature engineering → train → evaluate → deploy→monitor→repeat) on Azure + Databricks


• Standardize experimentation using MLflow or similar tools (tracking, artifacts, model registry, stages)


• Automate jobs with Databricks Workflows and CI/CD (GitHub Actions or Azure DevOps)


• Implement data & model observability: freshness/completeness, drift (features/model), training/serving skew, SLA/SLO monitoring


• Ensure security & compliance


• Handle incidents and post-mortems for ML pipelines and serving infrastructure

What you\'ll need

• Excellence in Python software engineering and developing tests


• Fundamental understanding of Machine Learning


• 3+ years in Data Eng/MLOps roles


• Strong PySpark


• Hands-on with Databricks and Delta Lake


• CI/CD for data/ML (Git, PR workflow, automated tests, environment pinning)


• Azure basics


• Monitoring and building dashboards


• Clear communication; operational-excellence mindset (SLA/SLO ownership)

What\'s nice to have

• Unity Catalog experience


• Databricks Feature Store


• Terraform for workspace/clusters/jobs/UC objects


• Telemetry domain exposure


• Optimize PySpark jobs (partitioning, caching, etc.) and cost (autoscaling, spot).