ML DevOps Engineer: Cloud, Pipelines & Scale

Pathway; Wrocław; On-site; PLN 336,000 - 560,000; Full time

Job summary

Pathway is seeking a Machine Learning DevOps engineer with strong cloud and compute-cluster management, capable of scaling infrastructures and administering Linux systems. The role focuses on operationalizing ML models, ensuring reliability, automation, and scalable pipelines in a multi-cloud environment.

You will optimize ML training/inference infrastructure, manage ML pipelines, track model versions, and collaborate with ML and software teams to support growing production workloads across

Qualifications

• Bachelor's degree in CS or IT or equivalent.
• Strong Linux and scripting skills required.
• Experience managing multi-cloud ML infra and CI/CD pipelines.
• Familiarity with ML workflows and orchestration tools.
• Willingness to learn and adapt in a fast-paced startup environment.

Responsibilities

• Optimize infrastructure for ML training and inference.
• Automate and maintain ML/LLM pipelines.
• Manage model versioning, reproducibility, and auditability.
• Scale clusters and resources for growing teams.
• Collaborate with ML engineers, software engineers, and platform teams.
• Implement ML-centric CI/CD practices.
• Monitor performance and data drift in production.

Skills

Linux

Shell scripting

Cluster configuration

Workload management

Containerization

Kubernetes

CI/CD

Cloud platforms

ML pipelines

Python (ML libs)

System & networks administration

Willingness to learn

Education

BSc in Computer Science or IT

Tools

MLflow

Kubeflow

Airflow

Metaflow

Terraform

CloudFormation

SageMaker Hyperpod

Vertex AI

Job description

Pathway is seeking a Machine Learning DevOps engineer with strong cloud and compute-cluster management, capable of scaling infrastructures and administering Linux systems. The role focuses on operationalizing ML models, ensuring reliability, automation, and scalable pipelines in a multi-cloud environment.

You will optimize ML training/inference infrastructure, manage ML pipelines, track model versions, and collaborate with ML and software teams to support growing production workloads across