
DS STREAM · Gdańsk, Warszawa, Kraków, Wrocław, Poznań
MLOps Production Engineer
W skrócie
zdania wybrane z oferty- 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.
- experience with Microsoft Azure tools, specifically Azure Data Factory,
Technologie
Pełny opis
z No Fluff JobsOpis oferty
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.
Wymagania
- 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.
Zakres obowiązków
- 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.
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