Senior MLOps 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:

MLOps Engineer is responsible for streamlining machine learning project lifecycles by designing and automating workflows, implementing CI/CD pipelines, ensuring reproducibility, and providing reliable experiment tracking. They collaborate with stakeholders and platform engineers to set up infrastructure, automate model deployment, monitor models, and scale training. MLOps Engineers possess a wide range of technical skills, including knowledge of orchestration, storage, containerization, observability, SQL, programming languages, cloud platforms, and data processing. Their expertise covers various ML algorithms and distributed training in environments like Spark, PyTorch, TensorFlow, Dask, and Ray. MLOps Engineers are essential for optimizing and maintaining efficient ML processes in organizations.

You will be:

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

• implementing ML workflows / automating CI/CD pipelines, 

• automating model deployment and implementing model monitoring, 

• collaborating with Platform Engineers to implement backup and disaster recovery processes for ML workflows, especially models and experiments, 

• collaborating with stakeholders to understand the key challenges and inefficiencies of Machine Learning project lifecycles within the company, 

• keeping on top of the latest trends and advancements in data engineering and machine learning.