Machine Learning Engineer

• Practice Data

• Technologies Business Intelligence Jobs and Data Recruitment

• Location Netherlands, - None Specified -

• Type Contract

In this role you will be a senior machine learning engineer in a cross-functional squad. On a typical day you will,

• Build machine learning solutions with global impact, helping business and consumers
• Work end-to-end on the ML lifecycle, from data exploration to model operationalization
• Collaborate with data engineers, data scientist, product etc in a multi-functional team to the delivery and maintenance of these solutions and business integration
• Partner with different teams and domains on designing, explaining and implementing ML models
• Play and active role in the research and innovation w.r.t. the applicability of ML to improve business objectives
• Participate in the design and execution of A/B testing, model competition etc.
• You will be responsible for, and support users with the solutions that you and the team has built
• Work with the MLOps engineer in your team on different the operationalization fo the models, which can be batch inference, or live through providing API’s
• Ensure high quality solutions are delivered, through testing, applying engineering standards and actively working on model monitoring

WHO YOU WILL WORK WITH

You will be part of an agile scrum team, closely working with the Product Owner, Scrum Master and other team members that drive to deliver innovative AI solutions  and our global consumers. You will partner with business facing team members and other technology solution delivery, architecture, and platform teams.

Together with all the ML engineers from the different squads you form the AI Engineering chapter. In this group you we work AI innovation topics, standard ways of working and improving our engineering game.

WHAT YOU BRING

• 5+ years of experience working with Machine Learning, and delivering business value through applying ML
• Advanced degree in computer science, math, statistics, engineering or a related degree
• Experience with Python, ML libraries (such as scikit-learn, pytorch, etc), SQL, Spark, pandas and cloud technologies
• Thorough understanding of applied statistics, both shallow and deep ML models, can clearly articulate model choice trade-offs, neural network architecture and performance metrics
• Experience in designing and running live model tests such as through A/B or multi-armed bandit testing
• Have strong knowledge of the whole model lifecycle from exploring data to bringing machine learning solutions to production and integrating 
• Experience with containerizing ML workloads, using docker and kubernetes
• Background in software engineering, and experience with CI/CD, testing & creating microservices is highly preferred

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