MLOps Engineer

• Practical
  experience with Microsoft Azure tools, specifically Azure Data Factory,
  Databricks, Azure ML.


• Familiarity
  with GitHub / Azure DevOps for version control and deployment pipelines.


• Experience
  with Power BI for reporting and ServiceNow (or similar ticketing systems)
  for incident tracking.


• Understanding
  of Machine Learning or mathematical optimization workflows from an
  operational support perspective.


• Experience
  working within defined SLA frameworks in a production environment.

We are looking for an Operations Engineer with a solid background in Data Science and Machine Learning to support our mature, fully deployed production algorithms. This role focuses on long-term operational stability, system health, and minor maintenance rather than developing new solutions from scratch.

The environment is highly stable with a low incident volume (typically around one incident per month). You will act as the dedicated technical expert for these production models, collaborating closely the internal product, architecture, and platform teams.

,[Monitor algorithm health, performance, and general operational workflows for deployed machine learning and optimization solutions., Investigate, troubleshoot, and resolve production incidents within the European time zone to meet defined SLA targets (ownership and closure)., Perform minor enhancements, maintenance tasks, and support occasional production deployments or periodic model refreshes., Maintain deep, model-specific knowledge to perform deeper technical investigations and refreshes when required.] Requirements: Python, Azure, Azure Data Factory, Docker Tools: . Additionally: Free coffee, In-house trainings.