AI Engineer (m/f/d) ML Ops & GPU Computing T Hub

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

Job ID: 3580441AI Engineer (m/f/d) ML Ops & GPU Computing T HubWarsawProfessionalApplication deadline: 30.12.2026Regular (Part Time Possible)T-Hub PolskaYour TaskWe are looking for an experienced AI Engineer who will be responsible for designing, deploying, and maintaining AI/ML solutions in production environments. The role combines expertise in Machine Learning Engineering, MLOps, and basic administration and optimization of GPU infrastructure supporting model training and inference workloads.Key ResponsibilitiesDesign and develop AI and Machine Learning solutions.Build and maintain MLOps pipelines (CI/CD for ML models).Automate model training, deployment, and monitoring processes.Manage the end-to-end machine learning lifecycle (ML Lifecycle Management).Collaborate closely with Data Scientists, Software Engineers, and business stakeholders.Optimize computational resource utilization, particularly GPU-based environments.Monitor model performance, quality, and data drift.Contribute to the design of AI architectures on cloud platforms.Create technical documentation and establish engineering best practices.Your ProfileMinimum 3 years of experience in AI/ML projects.Strong proficiency in Python.Hands-on experience with:PyTorch or TensorFlowScikit-learnHugging FaceExperience with MLOps technologies:MLflowKubeflowAzure Machine Learning, Vertex AI, or SageMakerCI/CD tools (GitHub Actions, Azure DevOps, GitLab CI)Experience with Docker and Kubernetes.Hands-on experience working with distributed AI/ML environments and compute clusters.Good understanding of cloud platforms (Azure, AWS, or GCP) or on-premise systems. Understanding of workload orchestration, resource allocation, and scaling for machine learning workloads.Experience with Git and modern DevOps practices.English proficiency at B2 level or higher.Strong ownership and accountability for delivered solutions.Ability to translate business requirements into technical implementations.Analytical mindset with a problem-solving approach.Passion for experimenting with emerging AI technologies.Strong organizational and collaboration skills in Agile environments.Nice-to-HaveExperience with LLMs and Generative AI solutions.Knowledge of RAG, Vector Databases, and Agentic AI.Experience with cloud-based GPU platforms (Azure AI, AWS EC2 P4/P5, GCP A3).Experience managing or operating GPU-enabled Kubernetes clusters.Experience with NVIDIA GPU Operator, KServe, Ray, Kubeflow, or similar technologies.Understanding of cluster monitoring and performance optimization in AI environments.Familiarity with Spark, Databricks, or Ray.