About the Project
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.
Responsibilities
•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
Requirements
•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
Nice to Have
Charts, Ingress, GCP, AWS, GitHub Actions, Jenkins, Shell, Ansible, Bicep, LLM, RAG, Prometheus, Grafana, Azure Monitor
Conditions
•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
