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Senior MLOps Engineer (AI/ML Platform)

6 days, 14 hours ago
Europe, Bulgaria, Poland, Romania
Senior
Machine Learning Engineer
Software Development
CI/CD FastAPI Flask GCP GitHub Actions Machine Learning MLflow MLOps Python PyTorch REST API Scikit-learn TensorFlow Terraform Vertex AI
Apply Now
Xebia

Xebia

Xebia is a pioneering IT consultancy company specializing in Continuous Delivery, DevOps, Agile Consulting, and Digital Transformation Services, creating digital leaders through innovative solutions.

Internet Software & Services
1K-5K
Founded 2008
View All Jobs 15

Description

  • Design, build, and manage AI/ML platform infrastructure on Google Cloud Platform using Vertex AI services.
  • Build and operate ML pipelines across model training, evaluation, deployment, and lifecycle management.
  • Provision and automate cloud infrastructure with Terraform and integrate it into CI/CD pipelines.
  • Develop and maintain CI/CD workflows with GitHub Actions for ML training, deployment, and retraining processes.
  • Develop production-grade Python solutions and design secure, scalable REST APIs.
  • Implement monitoring and observability for ML models, including performance tracking, data drift detection, and system health monitoring.
  • Collaborate with hybrid cloud and HPC environments to support GPU-based training and large-scale ML workloads.

Requirements

  • Proven experience in MLOps or ML Platform Engineering in production environments.
  • Hands-on experience with Google Cloud Platform, especially Vertex AI, and cloud-native ML workflows.
  • Experience with Terraform, specifically using the GCP provider for infrastructure as code.
  • Strong experience with Python 3.10+.
  • Experience with ML frameworks such as TensorFlow, PyTorch, and scikit-learn.
  • Experience with MLOps tools such as MLflow and KFP SDK.
  • Experience building secure, production-grade APIs using FastAPI or Flask.
  • Solid understanding of CI/CD pipelines, especially GitHub Actions.
  • Knowledge of identity and access management concepts, including OAuth 2.0 and WIF.
  • Experience with Slurm and HPC environments, including GPU scheduling.
  • Strong knowledge of ML lifecycle best practices, distributed training, and model deployment patterns.
  • Work authorization and the ability to work from the European Union region are required.

Interested in this position?

Apply directly on the company website

Apply Now

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