Machine Learning Engineer ID 2165

As a Machine Learning Engineer you will:

• Work on a key topic: Financial Economic Crime.
  
• Develop manage and maintain Azure infrastructure tailored for highperformance machine learning and artificial intelligence applications.
  
• Collaborate in a multidisciplined highly skilled team to develop scalable solutions and ML pipelines throughout the entire ML cycle.
  
• Ensure the health of the system and models by monitoring data quality performance and business impact and devising mitigation plans.
  
• Identify underlying issues and opportunities across the ML development cycle and introduce tools for efficient development and testing.
  
• Implement solutions in alignment with architectural guidelines in collaboration with your team.
  
• Continuously evolve your craft by staying up to date with the latest technologies.
  

What You Bring to the Table:

• A completed relevant HBO or WO education.
  
• Minimum of 4 years of relevant experience in Software Data or ML Engineering with at least 2 years specifically in ML Engineering.
  
• Experience working with large data sets using Python and PySpark (Azure Databricks experience is a plus).
  
• Software development skills and good practices such as Git release management unit tests and testing strategy.
  
• Experience working in a cloud environment preferably Azure.
  

You should possess the ability to:

• Be customeroriented driven and possess a strong analytical mind.
  
• Be a true team player and eager to learn new techniques and developments.
  
• Collaborate with data scientists and machine learning practitioners to provide infrastructure support for model development and deployment.
  

What We Bring to the Table:

• An opportunity to make a significant impact on Financial Economic Crime and Customer Due Diligence.
  
• A platformcentric approach to optimizing the ML Model workflow development and deployment.
  
• A focus on automating the entire Model Lifecycle to provide a stable platform that delivers models in a secure and regulatorycompliant way.
  
• An environment that encourages continuous learning and development with the latest market developments.
  
• A collaborative and supportive team culture.
  

As a Machine Learning Engineer, you will: Work on a key topic: Financial Economic Crime. Develop, manage, and maintain Azure infrastructure tailored for high-performance machine learning and artificial intelligence applications. Collaborate in a multi-disciplined, highly skilled team to develop scalable solutions and ML pipelines throughout the entire ML cycle. Ensure the health of the system and models by monitoring data quality, performance, and business impact, and devising mitigation plans. Identify underlying issues and opportunities across the ML development cycle and introduce tools for efficient development and testing. Implement solutions in alignment with architectural guidelines in collaboration with your team. Continuously evolve your craft by staying up to date with the latest technologies. What You Bring to the Table: A completed relevant HBO or WO education. Minimum of 4 years of relevant experience in Software, Data, or ML Engineering, with at least 2 years specifically in ML Engineering. Experience working with large data sets using Python and PySpark (Azure Data-bricks experience is a plus). Software development skills and good practices such as Git, release management, unit tests, and testing strategy. Experience working in a cloud environment, preferably Azure. You should possess the ability to: Be customer-oriented, driven, and possess a strong analytical mind. Be a true team player and eager to learn new techniques and developments. Collaborate with data scientists and machine learning practitioners to provide infrastructure support for model development and deployment. What We Bring to the Table: An opportunity to make a significant impact on Financial Economic Crime and Customer Due Diligence. A platform-centric approach to optimizing the ML Model workflow, development, and deployment. A focus on automating the entire Model Lifecycle to provide a stable platform that delivers models in a secure and regulatory-compliant way. An environment that encourages continuous learning and development with the latest market developments. A collaborative and supportive team culture.