AI Engineer: ML Ops & GPU Systems

Telekom; Województwo mazowieckie; On-site; PLN 120,000 - 180,000; Part time

Job summary

Telekom is seeking an experienced AI Engineer to design, deploy, and maintain AI/ML solutions in production. You will build MLOps pipelines, automate training and deployment, and manage the ML lifecycle with a focus on GPU infrastructure.

You will work closely with data scientists, software engineers and business stakeholders. You will optimize resource utilization across distributed GPU environments, monitor model performance and data drift, and contribute to AI architectures on cloud

Qualifications

• 3+ years of AI/ML project experience.
• Strong Python proficiency.
• Hands-on with PyTorch or TensorFlow.
• Experience with Scikit-learn and Hugging Face.
• Experience with MLOps technologies: MLflow, Kubeflow, Azure ML, Vertex AI, or SageMaker.
• CI/CD tools for ML: GitHub Actions, Azure DevOps, GitLab CI.
• Docker and Kubernetes experience.
• Distributed AI/ML environments and compute clusters.
• Cloud platforms (Azure, AWS, or GCP) or on‑premise systems.
• Git and modern DevOps practices.
• English proficiency at B2 level or higher.

Responsibilities

• Design and develop AI/ML solutions for production.
• Build and maintain MLOps pipelines for ML models.
• Automate training, deployment, and monitoring of models.
• Manage end-to-end ML lifecycle and resource optimization for GPUs.
• Collaborate with data scientists, engineers, and stakeholders.
• Document architectures and establish engineering best practices.

Skills

Python

MLflow

Kubeflow

MLOps

CI/CD for ML

Git

Hugging Face

PyTorch

TensorFlow

Scikit-learn

Distributed AI/ML

Cloud platforms

Tools

Docker

Kubernetes

Job description

Telekom is seeking an experienced AI Engineer to design, deploy, and maintain AI/ML solutions in production. You will build MLOps pipelines, automate training and deployment, and manage the ML lifecycle with a focus on GPU infrastructure.

You will work closely with data scientists, software engineers and business stakeholders. You will optimize resource utilization across distributed GPU environments, monitor model performance and data drift, and contribute to AI architectures on cloud