Senior MLOps Engineer (AI/ML Platform)

Xebia is a global AI-first, digital transformation, and engineering partner. With over 25 years of experience and a team of 5,000 professionals across 16 countries, we help organizations design and build scalable products, platforms, and data-driven solutions.

We specialize in Artificial Intelligence, Data and Cloud, Intelligent Automation, and Digital Products, combining deep technical expertise with a strong focus on engineering excellence and a people-first culture.

In the CEE region, we’re a team of nearly 1,000 experts delivering modern applications, data platforms, and AI solutions for clients such as McLaren, Aviva, Deloitte, Spotify, Disney, ING, UPS, Tesco, Truecaller, AllSaints, Volotea, Schmitz Cargobull, Allegro, InPost, and many, many more. We work with leading technologies including AWS, Azure, GCP, Databricks, and Snowflake, and combine strong engineering culture with a consulting mindset and a continuous focus on growth and knowledge sharing.

You will be:

• designing, building, and managing AI/ML platform infrastructure on Google Cloud Platform (GCP), leveraging Vertex AI services,
• building and operating ML pipelines, covering model training, evaluation, deployment, and lifecycle management,
• provisioning and automating cloud infrastructure using Terraform and integrating it into CI/CD pipelines,
• developing and maintaining CI/CD workflows with GitHub Actions and automating ML training, deployment, and retraining processes,
• developing production-grade Python solutions and designing secure, scalable REST APIs,
• implementing monitoring and observability for ML models, including performance tracking, data drift detection, and system health,
• collaborating with hybrid cloud and HPC environments to support GPU-based training and large-scale ML workloads.

Your profile:

• proven experience in MLOps / ML Platform Engineering in production environments,
• hands-on expertise with: GCP (Vertex AI) and cloud-native ML workflows and Terraform (GCP provider) for infrastructure as code,
• strong experience with: Python (3.10+), ML frameworks (TensorFlow, PyTorch, scikit-learn) and MLOps tools (MLflow, KFP SDK),
• experience building secure, production-grade APIs using FastAPI or Flask,
• solid understanding of: CI/CD pipelines (GitHub Actions), identity & access management (OAuth 2.0, WIF),
• experience with Slurm and HPC environments, including GPU scheduling,
• strong knowledge of: ML lifecycle best practices and distributed training and model deployment patterns.

Recruitment Process:

CV review – HR call – Technical Interview – Client Interview – Decision