MLOps Engineer

Quick Facts

• Role: MLOps Engineer

• Work model: Hybrid (1 day/week in the client office in Warsaw)

Description

Build and operate MLOps/LLMOps infrastructure for AI solutions in a production environment. You will create machine learning CI/CD pipelines, automate training and deployment workflows, containerize models, and implement monitoring such as data/model drift detection. The role focuses on Azure-based deployments and hybrid cloud integration.

Responsibilities

• Design, develop, and maintain MLOps/LLMOps infrastructure for AI solutions

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

• Create and evolve CI/CD/CT pipelines for machine learning

• Automate testing, training, deployment, and versioning of data and models

• Use tools such as MLflow, DVC, Kubeflow, and cloud-native capabilities

• Containerize AI/GenAI models with Docker and deploy on Kubernetes clusters

• Integrate cloud with on-premise systems in a hybrid architecture

• Implement model monitoring: drift detection, logging, and alerting

• Add auditability mechanisms (data/model lineage, access control, encryption)

• Optimize Azure resource usage, infrastructure costs, and model inference time

• Automate infrastructure using Infrastructure as Code

• Collaborate with Data Science, AI, and IT Operations teams; diagnose performance problems and production incidents

Requirements

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

• Hands-on experience deploying and maintaining Machine Learning models in production

• Advanced Docker and Kubernetes skills, including cluster management, Helm Charts, and Ingress

• Strong Microsoft Azure knowledge: Azure Machine Learning, AKS, Azure Container Registry (or ability to quickly ramp up if coming from AWS/GCP)

• Experience building CI/CD pipelines with Azure DevOps, GitHub Actions, or Jenkins

• Experience automating ML workflows (training and deployment) within pipelines

• Good knowledge of Python and Bash/Shell

• Practical knowledge of MLOps tools such as MLflow or Kubeflow (or equivalent cloud-native solutions)

• Infrastructure as Code experience with Terraform, Bicep, or Ansible

• Automation-first approach and proactive troubleshooting of performance and production incidents

• Experience with hybrid networking (VPN, VNet, Private Endpoints)

Benefits

• Support for professional and personal development

• Individual guidance from a Service Delivery Manager to plan your career path and ensure comfort on the project

• Training, certifications, and conferences: costs covered or subsidized

• #SmartChange to enable project changes aligned with your preferences

• Work-life balance initiatives: integration events, sports activities, and #edge1talks webinars

• Health package: private care, sports card, insurance, and psychological support