About the job
About Us
Xebia is a leading global technology company that began its journey in Central and Eastern Europe with two notable Polish firms: PGS Software, renowned for delivering exceptional cloud and software solutions, and GetInData, a trailblazer in the Big Data arena. Today, our dedicated team of over 1,000 professionals is committed to providing outstanding services across cloud, data, and software domains, and we're just getting started.
Our Mission
We undertake projects that are impactful and meaningful. Our expertise spans various industries including fintech, e-commerce, aviation, logistics, media, and fashion. We empower our clients to develop scalable platforms, data-driven solutions, and innovative applications utilizing Machine Learning (ML), Large Language Models (LLMs), and Generative AI. Our esteemed clientele includes industry giants such as Spotify, Disney, ING, UPS, Tesco, Truecaller, AllSaints, Volotea, Schmitz Cargobull, Allegro, and InPost.
Our Culture
What makes Xebia truly unique? Our vibrant community. We host events such as the Data & AI Warsaw Summit and organize meetups (Software Talks, Data Tech Talks). Our culture fosters growth through Guilds, Labs, and personal development budgets for both technical and soft skills. Here, it’s more than just a job; it’s a place for professional and personal growth.
Join Us
Experience our exceptional mindset, atmosphere, and team. Words can hardly capture what makes us special—come visit us and see for yourself.
Your Role:
Collaborate with Platform Engineers to establish the infrastructure necessary for efficient MLOps processes.
Implement machine learning workflows and automate CI/CD pipelines.
Automate model deployment and set up model monitoring.
Work with Platform Engineers to implement backup and disaster recovery strategies for ML workflows, focusing on models and experiments.
Engage with stakeholders to identify key challenges and inefficiencies in the Machine Learning project lifecycle.
Stay updated on the latest trends and advancements in data engineering and machine learning.

