Senior ML Engineer | EU

Hello, let’s meet!

We are Xebia – a place where experts grow. For nearly two decades now, we’ve been developing digital solutions for clients from many industries and places across the globe. Among the brands we’ve worked with are UPS, McLaren, Aviva, Deloitte, and many, many more.

We’re passionate about Cloud-based solutions. So much so, that we have a partnership with three of the largest Cloud providers in the business – Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). We even became the first AWS Premier Consulting Partner in Poland.

Formerly we were known as PGS Software. In 2021, we joined Xebia Group – a family of interlinked companies driven by the desire to make a difference in the world of technology.

Xebia stands for innovation, talented team members, and technological excellence. Xebia means worldwide recognition, and thought leadership. This regularly provides us with the opportunity to work on global, innovative projects.

Our mission can be captured in one word: Authority. We want to be recognized as the authority in our field of expertise.

What makes us stand out? It’s the little details, like our attitude, dedication to knowledge, and the belief in people’s potential – emphasizing every team members development. Obviously, these things are not easy to present on paper – so, make sure to visit us to see it with your own eyes!

Now, we’ve talked a lot about ourselves – but we’d love to hear more about you.

About the role:

ML Engineers share fundamental responsibilities in streamlining machine learning project lifecycles. They are dedicated to designing and automating workflows, implementing CI/CD pipelines, ensuring reproducibility, and providing reliable experiment tracking. Their responsibilities also include collaborating with stakeholders and platform engineers, leveraging expertise in infrastructure setup, model deployment, monitoring, and proficiency with cloud platforms and data processing.

You will be:

• playing a critical role in developing new algorithms and optimizing existing ones, 

• adding significant pieces of functionality to the application, largely based on user feedback, 

• optimizing ML pipelines, 

• taking the initiative and proposing new approaches, 

• designing and architecting machine learning workflows/machine learning lifecycle process, 

• implementing ML workflows / automating CI/CD pipelines, 

• collaborating with Platform Engineers to set the infrastructure required to run MLOps processes efficiently.