MLOps Engineer

Quick Facts

MLOps Engineer role within a strategic transformation program building an AI- and automation-driven ecosystem for insurance claims and benefits workflows.

Hybrid work model: at least 1 day per week in the office in Poland.

Description

You will design, deploy, and run machine learning workflows in production using MLOps practices. The work includes building CI/CD pipelines for ML (including training), managing model lifecycle with tools such as MLflow/Kubeflow, and deploying on containerized infrastructure in public cloud environments. You’ll collaborate closely with Data Science and IT Operations teams to deliver reliable production services.

Responsibilities

• Operate and deploy ML models in production
• Build CI/CD pipelines with ML training steps
• Manage container platforms using Docker and Kubernetes
• Use Helm charts and Ingress for Kubernetes deployments
• Implement Infrastructure as Code with Terraform/Bicep/Ansible
• Support ML model lifecycle management (MLflow/Kubeflow or cloud-native equivalents)
• Contribute to incident and performance issue resolution for production systems

Requirements

• Minimum 3 years of experience in DevOps, MLOps, or Software Engineering
• Hands-on ML model experience in production
• Advanced Docker and Kubernetes (cluster management, Helm, Ingress)
• Deep Azure experience (Azure ML, AKS, Azure Container Registry) or quick ramp-up to Azure from GCP/AWS
• CI/CD pipeline experience (Azure DevOps, GitHub Actions, Jenkins) with ML training as a pipeline step
• Python and Bash/Shell scripting skills
• MLOps tooling experience: MLflow, Kubeflow, or native cloud solutions
• Infrastructure as Code: Terraform, Bicep, or Ansible
• Ability to provide services from Poland with hybrid work (minimum 1 office day per week)
• Higher technical education in Computer Science/Telecommunications or related field
• Automation First mindset and ability to work across Data Science and IT Operations

Benefits

• Work on a strategically important program with tangible impact on the future of customer service
• Cross-functional team collaboration with multiple expert specializations