You will also collaborate closely with our Data Scientists, offering technical guidance in model development and supporting them with templates and reusable components. Your responsibilities as an MLOpsEngineer Industrialization of ML models and data features developed by Data Scientists Development & maintenance of framework and templates for model industrialization, supporting both batch scoring and live inference Development & maintenance of our Analytics Feature Store that serves as main source of information for ML models and ad hoc analyses Model and data quality monitoring - check availability and quality of model scores and feature values, spot and analyze issues, communicate to data scientists and Business stakeholders and ensure that problems get resolved swiftly Build and maintain a library of reusable components to facilitate and standardize model development and boost the efficiency of the Data Scientists Closely collaborate with Data Scientists during model development to offer technical guidance Our ideal MLOpsEngineer ..
MLOpsEngineers form the bridge between data science and data engineering and should therefore have a good mix of competencies from both worlds.
However, we do not expect candidates to already meet all the requirements listed below.
So, if you identify with the majority of the following attributes, don’t hesitate to apply! Master’s degree with a focus on computer science/informatics/machine learning (or equivalent) Good knowledge of either Machine Learning Engineering, Data Engineering, or DevOps, and at least basic knowledge of the other domains.
Working experience in any of these domains is a plus Good knowledge of programming and query languages (Python, PySpark, SQL), where familiarity with Python libraries commonly used in scientific computing and machine learning (pandas, scikit-learn, pytorch, etc.) is a plus Familiarity with the AWS Cloud environment (and AWS Sagemaker in particular), Snowflake and/or dbt are not strictly required, but definitely an added value Hands-on experience with Git A problem-solving mindset, strong self-discipline and willingness to take ownership of the operational health and reliability of our ML models Team player with good communication skills to interact with data scientists and be the first point of contact for Business stakeholders in case of issues or questions Willingness to learn and curiosity for new technologies and innovation Fluent English speaker What we offer Start a new adventure in an innovative, healthy and ambitious organization that aims to become an (inter)national frontrunner in leveraging AI and machine learning.
Enjoy flexible working hours and the freedom to work from home (3 days per week) or in our open and trendy collaboration spots at the office Work in a state-of-the-art AWS Cloud environment We’ll coach you, train you and make you part of our story.
You’ll have access to an extensive training catalogue and external platforms such as Datacamp and Linkedin Learning, and ample opportunity to learn on the job An agile organization that stimulates personal growth, internal mobility and the possibility to change roles Join a team of young, dynamic, enthusiastic and results-driven data profiles, with fun team buildings, on-site padel games and the occasional afterwork drinks Enjoy an attractive compensation & benefits package, including a company car, meal vouchers, group and health insurance, a yearly bonus and discounts on Telenet products. Telenet
