Senior Machine Learning full_time

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 & 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.

Send us your resume to start the conversation and join the #Xebia.

• implementing end-to-end solutions for batch and real-time algorithms along with requisite tooling around monitoring, logging, automated testing, model retraining, model deployment and metadata tracking,
• identifying new opportunities to improve business processes and improve consumer experiences, and prototype solutions to demonstrate value with a crawl, walk, run mindset,
• working with data scientists and analysts to create and deploy new product features on the ecommerce website, in-store portals and client’s mobile app,
• establishing scalable, efficient, automated processes for data analyses, model development, validation and implementation,
• writing efficient and scalable software to ship products in an iterative, continual-release environment,
• contributing to and promoting good software engineering practices across the team and build cloud native software for ML pipelines,
• contributing to and re-use community best practices.

• ability to start immediately,
• openness to work daily between till 19.00 pm CET,
• university or advanced degree in engineering, computer science, mathematics, or a related field,
• 7+ years experience developing and deploying machine learning systems into production,
• experience working with a variety of relational SQL and NoSQL databases,
• experience working with big data tools: Hadoop, Spark, Kafka, etc.,
• experience with at least one cloud provider solution (AWS, GCP, Azure) and understanding of severless code development
• experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc.,
• previous experience developing predictive models in a production environment, MLOps and model integration into larger scale applications,
• experience with Machine and Deep Learning libraries such as Scikit-learn, XGBoost, MXNet, TensorFlow or PyTorch,
• exposition to GenAI and solid understanding of multimodal AI via HuggingFace, Llama, VertexAI, AWS Bedrock or GPT,
• knowledge of data pipeline and workflow management tools,
• expertise in standard software engineering methodology, e.g. unit testing, test automation, continuous integration, code reviews, design documentation,
• working experience with native ML orchestration systems such as Kubeflow, Step Functions, MLflow, Airflow, TFX,
• very good verbal and written communication skills in English.
• good verbal and written communication skills in English.

Work from European Union region and work permit are required.

• relevant working experience with Docker and Kubernetes.