MLOps Engineer

Quick Facts

• Role: MLOps Engineer

• Focus: Designing and operating MLOps/LLMOps environments for an AI platform in the insurance domain

Description

You will design, build, and maintain MLOps/LLMOps infrastructure and automate model deployment workflows, ensuring stable operation in production. The work includes cloud and Kubernetes infrastructure, CI/CD automation, monitoring, and security for AI solutions. You will also support scalable training/serving and optimize costs, performance, and inference latency.

Responsibilities

• Design, develop, and maintain MLOps/LLMOps and the AI platform infrastructure

• Build a scalable environment for training and serving models using Azure Machine Learning, Azure AI Foundry, and Kubernetes/AKS

• Create and evolve CI/CD/CT pipelines covering automated testing, training, and versioning of data and models (e.g., DVC, MLflow)

• Prepare Docker images and deploy AI/GenAI models to Kubernetes clusters

• Integrate cloud environments with on-premises systems

• Develop monitoring and observability for models, including detecting Data Drift and Model Drift, logging, and alerting

• Implement auditability capabilities (model auditability, data lineage, access management, and AI security)

• Optimize cloud costs, performance, and inference time; scale infrastructure based on load

Requirements

• Minimum 3 years of experience in DevOps, MLOps, or Software Engineering

• Experience running ML models in production

• Excellent knowledge of Docker and Kubernetes (cluster management, Helm Charts, Ingress)

• Experience with Azure (Azure ML, AKS, Azure Container Registry) or ability to quickly ramp up on Azure

• Experience building CI/CD pipelines (Azure DevOps, GitHub Actions, Jenkins), including for ML processes

• Strong Python skills and Bash/Shell scripting

• Practical knowledge of MLOps tools such as MLflow, Kubeflow, or cloud-native equivalents

• Infrastructure as Code experience (Terraform, Bicep, or Ansible)

• Higher technical education (Computer Science, Telecommunications, or related)

• Automation First approach and willingness to automate manual processes

• Ability to collaborate with Data Science and IT Operations teams

• Proactive approach to troubleshooting performance issues and production incidents

Benefits

• B2B contract and long-term cooperation

• Private medical care (Luxmed)

• Multisport sports card

• Hybrid work model (1 day per week in the Warsaw office)

Recruitment Process

• HR interview (20 minutes)

• Client meeting (1 hour)