mlops engineer for insurance claims

The client is developing a modern claims and benefits handling ecosystem based on data, automation, AI, and workflow processes as part of a strategic business and technology transformation program.

• Implement CI/CD pipelines for ML solutions, including automated testing, data and model versioning, and Continuous Training
• Design and build scalable MLOps/LLMOps infrastructure for model training and serving using Azure Machine Learning, Azure AI Foundry, and Kubernetes (AKS)
• Prepare Docker images for AI/GenAI models and manage deployments on Kubernetes clusters in a hybrid architecture integrated with on-premise systems
• Manage Azure cloud resources, optimize model inference time, and scale infrastructure based on workload

• At least 3 years of experience in MLOps, Machine Learning, or Data Engineering
• Deep knowledge of Azure, especially Azure ML, AKS, and Azure Container Registry, or GCP/AWS with readiness to quickly ramp up on Azure
• Hands-on experience with MLflow, Kubeflow, or cloud-native solutions for model lifecycle management
• Advanced knowledge of Docker and Kubernetes, including cluster management, Helm charts, and Ingress
• Experience building pipelines with Azure DevOps, GitHub Actions, or Jenkins
• Good knowledge of Python and Bash/Shell
• Knowledge of Terraform, Bicep, or Ansible
• Будет плюсом: Charts, Ingress, GCP, AWS, GitHub Actions, Jenkins, Shell, Ansible, Bicep, LLM, RAG, Prometheus, Grafana, Azure Monitor

• Project-based cooperation, settled by milestones and acceptance protocols for individual deliverables
• Administrative and settlement support from Connectis
• Participation in industry events and technology meetups
• 5 000 PLN referral bonus for recommending people to the company’s projects
• Flexible B2B cooperation
• Fast and remote recruitment process